Bump vLLM to 0.17.0

Merge pull request #273 from runpod-workers/bug/hf-overides-rope-scaling
bug: fix rope scaling to be forward compatible from hf_overrides
2026-03-26 12:13:42 -05:00 · 2026-03-10 11:21:44 -05:00 · 2026-03-06 15:34:11 -06:00 · 2026-03-05 13:06:45 -06:00 · 2026-03-04 16:38:40 -06:00 · 2026-02-27 15:34:23 -06:00
27 changed files with 3450 additions and 412 deletions
@@ -1,3 +0,0 @@
-MODEL_NAME="mistralai/Mistral-7B-Instruct-v0.1"
-MODEL_BASE_PATH="./models"
-DISABLE_LOG_STATS=0
@@ -0,0 +1,79 @@
+# Contributing to worker-vllm
+
+## 🚀 Release Process
+
+### Development Workflow
+
+1. **Feature Development**
+
+   ```bash
+   git checkout -b feature/your-feature-name
+   # Make your changes
+   git push origin feature/your-feature-name
+   ```
+
+   - Creates pull request → triggers dev build: `runpod/worker-v1-vllm:dev-refs-pull-214-merge`
+
+2. **Main Branch**
+   ```bash
+   git checkout main
+   git merge feature/your-feature-name
+   git push origin main
+   ```
+   - No automatic builds on main (staging area)
+
+### Creating Releases
+
+**Method 1: GitHub UI (Recommended)**
+
+1. Go to [Releases](https://github.com/runpod-workers/worker-vllm/releases)
+2. Click **"Create a new release"**
+3. **Tag version**: `v2.8.0` (with "v" prefix, semantic versioning)
+4. **Target**: `main` branch
+5. **Title**: `Release 2.8.0`
+6. **Description**: Brief changelog
+7. Click **"Publish release"**
+
+**Method 2: Git CLI**
+
+```bash
+git checkout main
+git tag v2.8.0
+git push origin v2.8.0
+```
+
+### What Happens Automatically
+
+✅ **GitHub Release** created (if using Method 1)  
+✅ **Docker Image** built and pushed: `runpod/worker-v1-vllm:v2.8.0`  
+✅ **Documentation** updated with new version references
+
+## 📋 Version Format
+
+- **Format**: `vMAJOR.MINOR.PATCH` (e.g., `v2.8.0`)
+- **With "v" prefix**: Use `v2.8.0` for git tags
+- **Semantic Versioning**: Follow [SemVer](https://semver.org/)
+
+## 🐛 Development
+
+### Running Tests
+
+```bash
+# Update test configuration in .runpod/tests.json
+# Tests run automatically via RunPod platform
+```
+
+### Model Updates
+
+- Update `MODEL_NAME` in `.runpod/tests.json` and `worker-config.json`
+- Ensure model has vLLM support and chat template (for OpenAI compatibility)
+
+### Environment Variables
+
+See [README.md](../README.md) for full list of supported environment variables.
+
+## 🔧 CI/CD Workflows
+
+- **Dev builds**: All pull requests → `dev-refs-pull-<PR#>-merge` images
+- **Release builds**: Git tags → versioned images + GitHub releases
+- **Manual triggers**: Available in GitHub Actions for emergency releases
@@ -19,32 +19,49 @@ jobs:

      - name: Check for new package version and update
        run: |
-          # Get current version
-          current_version=$(grep -oP 'runpod==\K[^"]+' ./builder/requirements.txt)
+          echo "Fetching the current runpod version from requirements.txt..."
+          
+          # Get current version, allowing both == and ~= in the search pattern
+          current_version=$(grep -oP 'runpod[~=]{1,2}\K[^"]+' ./builder/requirements.txt)
+          echo "Current version: $current_version"

-          # Get new version
+          # Extract major and minor from current version
+          current_major_minor=$(echo $current_version | cut -d. -f1,2)
+          echo "Current major.minor: $current_major_minor"
+
+          echo "Fetching the latest runpod version from PyPI..."
+          
+          # Get new version from PyPI
          new_version=$(curl -s https://pypi.org/pypi/runpod/json | jq -r .info.version)
          echo "NEW_VERSION_ENV=$new_version" >> $GITHUB_ENV
+          echo "New version: $new_version"
+
+          # Extract major and minor from new version
+          new_major_minor=$(echo $new_version | cut -d. -f1,2)
+          echo "New major.minor: $new_major_minor"

          if [ -z "$new_version" ]; then
-              echo "Failed to fetch the new version."
+              echo "ERROR: Failed to fetch the new version from PyPI."
              exit 1
          fi

-          # Check if the version is already up-to-date
-          if [ "$current_version" = "$new_version" ]; then
-              echo "The package version is already up-to-date."
+          # Check if the major or minor version is different
+          if [ "$current_major_minor" = "$new_major_minor" ]; then
+              echo "No update needed. The new version ($new_major_minor) is within the allowed range (~= $current_major_minor)."
              exit 0
          fi

-          # Update requirements.txt
-          sed -i "s/runpod==.*/runpod==$new_version/" ./builder/requirements.txt
+          echo "New major/minor detected ($new_major_minor). Updating requirements.txt..."
+
+          # Update requirements.txt, preserving the existing constraint type (~= or ==)
+          sed -i "s/runpod[~=][^ ]*/runpod~=$new_version/" ./builder/requirements.txt
+          echo "requirements.txt has been updated."

      - name: Create Pull Request
        uses: peter-evans/create-pull-request@v3
        with:
          token: ${{ secrets.GITHUB_TOKEN }}
-          commit-message: Update package version
+          commit-message: Update runpod package version
          title: Update runpod package version
          body: The package version has been updated to ${{ env.NEW_VERSION_ENV }}
          branch: runpod-package-update
@@ -0,0 +1,60 @@
+name: Development
+
+on:
+  pull_request:
+    branches:
+      - "**"
+
+permissions:
+  contents: read
+
+jobs:
+  dev:
+    runs-on: [blacksmith-8vcpu-ubuntu-2204, linux]
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Clear space to remove unused folders
+        run: |
+          rm -rf /usr/share/dotnet
+          rm -rf /opt/ghc
+          rm -rf "/usr/local/share/boost"
+          rm -rf "$AGENT_TOOLSDIRECTORY"
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: blacksmith docker layer cache
+        uses: useblacksmith/build-push-action@v1
+        with:
+          setup-only: true
+
+      - name: Set environment variables
+        run: |
+          echo "DOCKERHUB_REPO=${{ vars.DOCKERHUB_REPO || 'runpod' }}" >> $GITHUB_ENV
+          echo "DOCKERHUB_IMG=${{ vars.DOCKERHUB_IMG || 'worker-v1-vllm' }}" >> $GITHUB_ENV
+          echo "HUGGINGFACE_ACCESS_TOKEN=${{ secrets.HUGGINGFACE_ACCESS_TOKEN }}" >> $GITHUB_ENV
+          # Convert branch name to safe docker tag (replace / with -)
+          BRANCH_NAME="${GITHUB_REF##refs/heads/}"
+          SAFE_BRANCH_NAME=$(echo "$BRANCH_NAME" | sed 's/[^a-zA-Z0-9._-]/-/g' | sed 's/--*/-/g')
+          echo "RELEASE_VERSION=dev-${SAFE_BRANCH_NAME}" >> $GITHUB_ENV
+
+      - name: Build and push the images to Docker Hub
+        uses: docker/bake-action@v2
+        with:
+          push: true
+          set: |
+            *.args.DOCKERHUB_REPO=${{ env.DOCKERHUB_REPO }}
+            *.args.DOCKERHUB_IMG=${{ env.DOCKERHUB_IMG }}
+            *.args.RELEASE_VERSION=${{ env.RELEASE_VERSION }}
+            *.args.HUGGINGFACE_ACCESS_TOKEN=${{ env.HUGGINGFACE_ACCESS_TOKEN }}
@@ -1,40 +0,0 @@
-name: CD | Docker-Build-Release
-
-on:
-  push:
-    branches:
-      - "main"
-  release:
-    types: [published]
-  workflow_dispatch:
-    inputs:
-      image_tag:
-        description: "Docker Image Tag"
-        required: false
-        default: "dev"
-
-jobs:
-  docker-build:
-    runs-on: DO
-    # DO is a custom runner deployed on DigitalOcean, only available for workflows under the runpod-workers organization.
-    # If you would like to use this workflow, you can replace DO with ubuntu-latest or any other runner.
-
-    steps:
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v2
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
-
-      - name: Login to Docker Hub
-        uses: docker/login-action@v2
-        with:
-          username: ${{ secrets.DOCKERHUB_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-
-      # Build and push step
-      - name: Build and push
-        uses: docker/build-push-action@v4
-        with:
-          push: true
-          tags: ${{ vars.DOCKERHUB_REPO }}/${{ vars.DOCKERHUB_IMG }}:${{ (github.event_name == 'release' && github.event.release.tag_name) || (github.event_name == 'workflow_dispatch' && github.event.inputs.image_tag) || 'dev' }}
@@ -0,0 +1,86 @@
+name: Release
+
+on:
+  push:
+    tags:
+      - "v[0-9]+.[0-9]+.[0-9]+*" # Trigger on version tags like v1.0.0, v2.1.0, etc.
+  workflow_dispatch:
+    inputs:
+      version:
+        description: "Version to release (e.g., v2.8.0)"
+        required: true
+        type: string
+
+permissions:
+  contents: write # Required for creating GitHub releases
+
+jobs:
+  release:
+    runs-on: [blacksmith-8vcpu-ubuntu-2204, linux]
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Clear space to remove unused folders
+        run: |
+          rm -rf /usr/share/dotnet
+          rm -rf /opt/ghc
+          rm -rf "/usr/local/share/boost"
+          rm -rf "$AGENT_TOOLSDIRECTORY"
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: blacksmith docker layer cache
+        uses: useblacksmith/build-push-action@v1
+        with:
+          setup-only: true
+
+      - name: Set environment variables
+        run: |
+          echo "DOCKERHUB_REPO=${{ vars.DOCKERHUB_REPO || 'runpod' }}" >> $GITHUB_ENV
+          echo "DOCKERHUB_IMG=${{ vars.DOCKERHUB_IMG || 'worker-v1-vllm' }}" >> $GITHUB_ENV
+          echo "HUGGINGFACE_ACCESS_TOKEN=${{ secrets.HUGGINGFACE_ACCESS_TOKEN }}" >> $GITHUB_ENV
+
+          # Determine version based on trigger type
+          if [[ "${{ github.event_name }}" == "workflow_dispatch" ]]; then
+            # Manual trigger: use input version
+            VERSION="${{ github.event.inputs.version }}"
+            echo "RELEASE_VERSION=${VERSION}" >> $GITHUB_ENV
+            echo "IS_MANUAL_RELEASE=true" >> $GITHUB_ENV
+          else
+            # Tag trigger: use tag name (remove refs/tags/ prefix)
+            VERSION=${GITHUB_REF#refs/tags/}
+            echo "RELEASE_VERSION=${VERSION}" >> $GITHUB_ENV
+            echo "IS_MANUAL_RELEASE=false" >> $GITHUB_ENV
+          fi
+
+      - name: Build and push the images to Docker Hub
+        uses: docker/bake-action@v2
+        with:
+          push: true
+          set: |
+            *.args.DOCKERHUB_REPO=${{ env.DOCKERHUB_REPO }}
+            *.args.DOCKERHUB_IMG=${{ env.DOCKERHUB_IMG }}
+            *.args.RELEASE_VERSION=${{ env.RELEASE_VERSION }}
+            *.args.HUGGINGFACE_ACCESS_TOKEN=${{ env.HUGGINGFACE_ACCESS_TOKEN }}
+
+      - name: Release Summary
+        run: |
+          echo "🚀 Release completed!"
+          echo "Version: ${{ env.RELEASE_VERSION }}"
+          echo "Docker Image: ${{ env.DOCKERHUB_REPO }}/${{ env.DOCKERHUB_IMG }}:${{ env.RELEASE_VERSION }}"
+          if [[ "${{ github.event_name }}" == "workflow_dispatch" ]]; then
+            echo "Trigger: Manual workflow dispatch"
+          else
+            echo "Trigger: GitHub release (tag: ${{ github.ref_name }})"
+          fi
@@ -2,4 +2,6 @@
 runpod.toml
 *.pyc
 .env
-test/*
+test/*
+vllm-base/vllm-*
+.DS_Store
@@ -0,0 +1,263 @@
+![vLLM worker banner](https://cpjrphpz3t5wbwfe.public.blob.vercel-storage.com/worker-vllm_banner.jpeg)
+
+Run LLMs using [vLLM](https://docs.vllm.ai) with an OpenAI-compatible API
+
+---
+
+[![RunPod](https://api.runpod.io/badge/runpod-workers/worker-vllm)](https://www.runpod.io/console/hub/runpod-workers/worker-vllm)
+
+---
+
+## Endpoint Configuration
+
+All behaviour is controlled through environment variables:
+
+| Environment Variable                | Description                                       | Default             | Options                                                            |
+| ----------------------------------- | ------------------------------------------------- | ------------------- | ------------------------------------------------------------------ |
+| `MODEL_NAME`                        | Path of the model weights                         | "facebook/opt-125m" | Local folder or Hugging Face repo ID                               |
+| `HF_TOKEN`                          | HuggingFace access token for gated/private models |                     | Your HuggingFace access token                                      |
+| `MAX_MODEL_LEN`                     | Model's maximum context length                    |                     | Integer (e.g., 4096)                                               |
+| `QUANTIZATION`                      | Quantization method                               |                     | "awq", "gptq", "squeezellm", "bitsandbytes"                        |
+| `TENSOR_PARALLEL_SIZE`              | Number of GPUs                                    | 1                   | Integer                                                            |
+| `GPU_MEMORY_UTILIZATION`            | Fraction of GPU memory to use                     | 0.95                | Float between 0.0 and 1.0                                          |
+| `MAX_NUM_SEQS`                      | Maximum number of sequences per iteration         | 256                 | Integer                                                            |
+| `CUSTOM_CHAT_TEMPLATE`              | Custom chat template override                     |                     | Jinja2 template string                                             |
+| `ENABLE_AUTO_TOOL_CHOICE`           | Enable automatic tool selection                   | false               | boolean (true or false)                                            |
+| `TOOL_CALL_PARSER`                  | Parser for tool calls                             |                     | "mistral", "hermes", "llama3_json", "granite", "deepseek_v3", etc. |
+| `REASONING_PARSER`                  | Parser for reasoning-capable models               |                     | "deepseek_r1", "qwen3", "granite", "hunyuan_a13b"                  |
+| `OPENAI_SERVED_MODEL_NAME_OVERRIDE` | Override served model name in API                 |                     | String                                                             |
+| `MAX_CONCURRENCY`                   | Maximum concurrent requests                       | 300                 | Integer                                                            |
+
+**Pass any vLLM engine arg** not listed above by setting an env var with the **UPPERCASED** field name (e.g. `MAX_MODEL_LEN=4096`, `ENABLE_CHUNKED_PREFILL=true`). The worker auto-discovers all `AsyncEngineArgs` fields from env. See the [vLLM engine args docs](https://docs.vllm.ai/en/latest/configuration/engine_args) for all available options.
+
+For complete configuration options, see the [full configuration documentation](https://github.com/runpod-workers/worker-vllm/blob/main/docs/configuration.md).
+
+## API Usage
+
+This worker supports two API formats: **RunPod native** and **OpenAI-compatible**.
+
+### RunPod Native API
+
+For testing directly in the RunPod UI, use these examples in your endpoint's request tab.
+
+#### Chat Completions
+
+```json
+{
+  "input": {
+    "messages": [
+      { "role": "system", "content": "You are a helpful assistant." },
+      { "role": "user", "content": "What is the capital of France?" }
+    ],
+    "sampling_params": {
+      "max_tokens": 100,
+      "temperature": 0.7
+    }
+  }
+}
+```
+
+#### Chat Completions (Streaming)
+
+```json
+{
+  "input": {
+    "messages": [
+      { "role": "user", "content": "Write a short story about a robot." }
+    ],
+    "sampling_params": {
+      "max_tokens": 500,
+      "temperature": 0.8
+    },
+    "stream": true
+  }
+}
+```
+
+#### Text Generation
+
+For direct text generation without chat format:
+
+```json
+{
+  "input": {
+    "prompt": "The capital of France is",
+    "sampling_params": {
+      "max_tokens": 64,
+      "temperature": 0.0
+    }
+  }
+}
+```
+
+#### List Models
+
+```json
+{
+  "input": {
+    "openai_route": "/v1/models"
+  }
+}
+```
+
+---
+
+### OpenAI-Compatible API
+
+For external clients and SDKs, use the `/openai/v1` path prefix with your RunPod API key.
+
+#### Chat Completions
+
+**Path:** `/openai/v1/chat/completions`
+
+```json
+{
+  "model": "meta-llama/Llama-2-7b-chat-hf",
+  "messages": [
+    { "role": "system", "content": "You are a helpful assistant." },
+    { "role": "user", "content": "What is the capital of France?" }
+  ],
+  "max_tokens": 100,
+  "temperature": 0.7
+}
+```
+
+#### Chat Completions (Streaming)
+
+```json
+{
+  "model": "meta-llama/Llama-2-7b-chat-hf",
+  "messages": [
+    { "role": "user", "content": "Write a short story about a robot." }
+  ],
+  "max_tokens": 500,
+  "temperature": 0.8,
+  "stream": true
+}
+```
+
+#### Text Completions
+
+**Path:** `/openai/v1/completions`
+
+```json
+{
+  "model": "meta-llama/Llama-2-7b-chat-hf",
+  "prompt": "The capital of France is",
+  "max_tokens": 100,
+  "temperature": 0.7
+}
+```
+
+#### List Models
+
+**Path:** `/openai/v1/models`
+
+```json
+{}
+```
+
+#### Response Format
+
+Both APIs return the same response format:
+
+```json
+{
+  "choices": [
+    {
+      "index": 0,
+      "message": { "role": "assistant", "content": "Paris." },
+      "finish_reason": "stop"
+    }
+  ],
+  "usage": { "prompt_tokens": 9, "completion_tokens": 1, "total_tokens": 10 }
+}
+```
+
+---
+
+## Usage
+
+Below are minimal `python` snippets so you can copy-paste to get started quickly.
+
+> Replace `<ENDPOINT_ID>` with your endpoint ID and `<API_KEY>` with a [RunPod API key](https://docs.runpod.io/get-started/api-keys).
+
+### OpenAI compatible API
+
+Minimal Python example using the official `openai` SDK:
+
+```python
+from openai import OpenAI
+import os
+
+# Initialize the OpenAI Client with your RunPod API Key and Endpoint URL
+client = OpenAI(
+    api_key=os.getenv("RUNPOD_API_KEY"),
+    base_url=f"https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1",
+)
+```
+
+`Chat Completions (Non-Streaming)`
+
+```python
+response = client.chat.completions.create(
+    model="meta-llama/Llama-2-7b-chat-hf",
+    messages=[{"role": "user", "content": "Explain quantum computing in simple terms"}],
+    temperature=0,
+    max_tokens=100,
+)
+print(f"Response: {response.choices[0].message.content}")
+```
+
+`Chat Completions (Streaming)`
+
+```python
+response_stream = client.chat.completions.create(
+    model="meta-llama/Llama-2-7b-chat-hf",
+    messages=[{"role": "user", "content": "Explain quantum computing in simple terms"}],
+    temperature=0,
+    max_tokens=100,
+    stream=True
+)
+for response in response_stream:
+    print(response.choices[0].delta.content or "", end="", flush=True)
+```
+
+### RunPod Native API
+
+```python
+import requests
+
+response = requests.post(
+    "https://api.runpod.ai/v2/<ENDPOINT_ID>/run",
+    headers={"Authorization": "Bearer <API_KEY>"},
+    json={
+        "input": {
+            "messages": [
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": "Explain quantum computing in simple terms"}
+            ],
+            "sampling_params": {
+                "temperature": 0.7,
+                "max_tokens": 150
+            }
+        }
+    }
+)
+
+result = response.json()
+print(result["output"])
+```
+
+## Compatibility
+
+For supported models, see the [vLLM supported models documentation](https://docs.vllm.ai/en/latest/models/supported_models.html).
+
+Anything not recognized by worker-vllm is forwarded to vLLM's engine, so advanced options in the vLLM docs (guided generation, LoRA, speculative decoding, etc.) also work.
+
+## Documentation
+
+- **[🚀 Deployment Guide](https://docs.runpod.io/serverless/vllm/get-started)** - Step-by-step setup
+- **[📖 Configuration Reference](https://github.com/runpod-workers/worker-vllm/blob/main/docs/configuration.md)** - All environment variables
+- **[🏗️ Advanced Deployment](https://github.com/runpod-workers/worker-vllm/blob/main/docs/deployment.md)** - Custom builds and strategies
+- **[🔧 Development Guide](https://github.com/runpod-workers/worker-vllm/blob/main/docs/conventions.md)** - Architecture and patterns
@@ -0,0 +1,801 @@
+{
+  "title": "vLLM",
+  "description": "Deploy OpenAI-Compatible Blazing-Fast LLM Endpoints powered by vLLM",
+  "type": "serverless",
+  "category": "language",
+  "iconUrl": "https://registry.npmmirror.com/@lobehub/icons-static-png/latest/files/dark/vllm-color.png",
+  "config": {
+    "runsOn": "GPU",
+    "containerDiskInGb": 150,
+    "gpuIds": "ADA_80_PRO,AMPERE_80",
+    "gpuCount": 1,
+    "allowedCudaVersions": ["12.9", "12.8"],
+    "presets": [
+      {
+        "name": "deepseek-ai/deepseek-r1-distill-llama-8b",
+        "defaults": {
+          "MODEL_NAME": "deepseek-ai/deepseek-r1-distill-llama-8b"
+        }
+      }
+    ],
+    "env": [
+      {
+        "key": "MODEL_NAME",
+        "input": {
+          "name": "Model",
+          "type": "huggingface",
+          "description": "Hugging Face model name",
+          "required": true
+        }
+      },
+      {
+        "key": "TOKENIZER",
+        "input": {
+          "name": "Tokenizer",
+          "type": "string",
+          "description": "Name or path of the Hugging Face tokenizer to use.",
+          "advanced": true
+        }
+      },
+      {
+        "key": "TOKENIZER_MODE",
+        "input": {
+          "name": "Tokenizer Mode",
+          "type": "string",
+          "description": "The tokenizer mode.",
+          "options": [
+            {
+              "label": "auto",
+              "value": "auto"
+            },
+            {
+              "label": "slow",
+              "value": "slow"
+            }
+          ],
+          "default": "auto",
+          "advanced": true
+        }
+      },
+      {
+        "key": "SKIP_TOKENIZER_INIT",
+        "input": {
+          "name": "Skip Tokenizer Init",
+          "type": "boolean",
+          "description": "Skip initialization of tokenizer and detokenizer.",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "TRUST_REMOTE_CODE",
+        "input": {
+          "name": "Trust Remote Code",
+          "type": "boolean",
+          "description": "Trust remote code from Hugging Face.",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "DOWNLOAD_DIR",
+        "input": {
+          "name": "Download Directory",
+          "type": "string",
+          "description": "Directory to download and load the weights.",
+          "advanced": true
+        }
+      },
+      {
+        "key": "LOAD_FORMAT",
+        "input": {
+          "name": "Load Format",
+          "type": "string",
+          "description": "The format of the model weights to load.",
+          "options": [
+            {
+              "label": "auto",
+              "value": "auto"
+            },
+            {
+              "label": "pt",
+              "value": "pt"
+            },
+            {
+              "label": "safetensors",
+              "value": "safetensors"
+            },
+            {
+              "label": "npcache",
+              "value": "npcache"
+            },
+            {
+              "label": "dummy",
+              "value": "dummy"
+            },
+            {
+              "label": "tensorizer",
+              "value": "tensorizer"
+            },
+            {
+              "label": "bitsandbytes",
+              "value": "bitsandbytes"
+            }
+          ],
+          "advanced": true
+        }
+      },
+      {
+        "key": "DTYPE",
+        "input": {
+          "name": "Data Type",
+          "type": "string",
+          "description": "Data type for model weights and activations.",
+          "options": [
+            {
+              "label": "auto",
+              "value": "auto"
+            },
+            {
+              "label": "half",
+              "value": "half"
+            },
+            {
+              "label": "float16",
+              "value": "float16"
+            },
+            {
+              "label": "bfloat16",
+              "value": "bfloat16"
+            },
+            {
+              "label": "float",
+              "value": "float"
+            },
+            {
+              "label": "float32",
+              "value": "float32"
+            }
+          ],
+          "default": "auto",
+          "advanced": true
+        }
+      },
+      {
+        "key": "KV_CACHE_DTYPE",
+        "input": {
+          "name": "KV Cache Data Type",
+          "type": "string",
+          "description": "Data type for KV cache storage.",
+          "options": [
+            {
+              "label": "auto",
+              "value": "auto"
+            },
+            {
+              "label": "fp8",
+              "value": "fp8"
+            }
+          ],
+          "default": "auto",
+          "advanced": true
+        }
+      },
+      {
+        "key": "MAX_MODEL_LEN",
+        "input": {
+          "name": "Max Model Length",
+          "type": "number",
+          "description": "Model context length.",
+          "default": null,
+          "advanced": true
+        }
+      },
+      {
+        "key": "DISTRIBUTED_EXECUTOR_BACKEND",
+        "input": {
+          "name": "Distributed Executor Backend",
+          "type": "string",
+          "description": "Backend to use for distributed serving.",
+          "options": [
+            {
+              "label": "ray",
+              "value": "ray"
+            },
+            {
+              "label": "mp",
+              "value": "mp"
+            }
+          ],
+          "advanced": true,
+          "default": "mp"
+        }
+      },
+      {
+        "key": "RAY_WORKERS_USE_NSIGHT",
+        "input": {
+          "name": "Ray Workers Use Nsight",
+          "type": "boolean",
+          "description": "If specified, use nsight to profile Ray workers.",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "PIPELINE_PARALLEL_SIZE",
+        "input": {
+          "name": "Pipeline Parallel Size",
+          "type": "number",
+          "description": "Number of pipeline stages.",
+          "default": 1,
+          "advanced": true
+        }
+      },
+      {
+        "key": "TENSOR_PARALLEL_SIZE",
+        "input": {
+          "name": "Tensor Parallel Size",
+          "type": "number",
+          "description": "Number of tensor parallel replicas.",
+          "default": 1,
+          "advanced": true
+        }
+      },
+      {
+        "key": "MAX_PARALLEL_LOADING_WORKERS",
+        "input": {
+          "name": "Max Parallel Loading Workers",
+          "type": "number",
+          "description": "Load model sequentially in multiple batches.",
+          "advanced": true
+        }
+      },
+      {
+        "key": "ENABLE_PREFIX_CACHING",
+        "input": {
+          "name": "Enable Prefix Caching",
+          "type": "boolean",
+          "description": "Enables automatic prefix caching.",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "DISABLE_SLIDING_WINDOW",
+        "input": {
+          "name": "Disable Sliding Window",
+          "type": "boolean",
+          "description": "Disables sliding window, capping to sliding window size.",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "SEED",
+        "input": {
+          "name": "Seed",
+          "type": "number",
+          "description": "Random seed for operations.",
+          "default": 0,
+          "advanced": true
+        }
+      },
+      {
+        "key": "MAX_NUM_BATCHED_TOKENS",
+        "input": {
+          "name": "Max Num Batched Tokens",
+          "type": "number",
+          "description": "Maximum number of batched tokens per iteration.",
+          "default": null,
+          "advanced": true
+        }
+      },
+      {
+        "key": "MAX_NUM_SEQS",
+        "input": {
+          "name": "Max Num Seqs",
+          "type": "number",
+          "description": "Maximum number of sequences per iteration.",
+          "default": 256,
+          "advanced": true
+        }
+      },
+      {
+        "key": "MAX_LOGPROBS",
+        "input": {
+          "name": "Max Logprobs",
+          "type": "number",
+          "description": "Max number of log probs to return when logprobs is specified in SamplingParams.",
+          "default": 20,
+          "advanced": true
+        }
+      },
+      {
+        "key": "DISABLE_LOG_STATS",
+        "input": {
+          "name": "Disable Log Stats",
+          "type": "boolean",
+          "description": "Disable logging statistics.",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "QUANTIZATION",
+        "input": {
+          "name": "Quantization",
+          "type": "string",
+          "description": "Method used to quantize the weights.",
+          "options": [
+            {
+              "label": "None",
+              "value": "None"
+            },
+            {
+              "label": "AWQ",
+              "value": "awq"
+            },
+            {
+              "label": "SqueezeLLM",
+              "value": "squeezellm"
+            },
+            {
+              "label": "GPTQ",
+              "value": "gptq"
+            }
+          ],
+          "advanced": true
+        }
+      },
+      {
+        "key": "ENABLE_LORA",
+        "input": {
+          "name": "Enable LoRA",
+          "type": "boolean",
+          "description": "If True, enable handling of LoRA adapters.",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "MAX_LORAS",
+        "input": {
+          "name": "Max LoRAs",
+          "type": "number",
+          "description": "Max number of LoRAs in a single batch.",
+          "default": 1,
+          "advanced": true
+        }
+      },
+      {
+        "key": "MAX_LORA_RANK",
+        "input": {
+          "name": "Max LoRA Rank",
+          "type": "number",
+          "description": "Max LoRA rank.",
+          "default": 16,
+          "advanced": true
+        }
+      },
+      {
+        "key": "LORA_DTYPE",
+        "input": {
+          "name": "LoRA Data Type",
+          "type": "string",
+          "description": "Data type for LoRA.",
+          "options": [
+            {
+              "label": "auto",
+              "value": "auto"
+            },
+            {
+              "label": "float16",
+              "value": "float16"
+            },
+            {
+              "label": "bfloat16",
+              "value": "bfloat16"
+            },
+            {
+              "label": "float32",
+              "value": "float32"
+            }
+          ],
+          "default": "auto",
+          "advanced": true
+        }
+      },
+      {
+        "key": "MAX_CPU_LORAS",
+        "input": {
+          "name": "Max CPU LoRAs",
+          "type": "number",
+          "description": "Maximum number of LoRAs to store in CPU memory.",
+          "advanced": true
+        }
+      },
+      {
+        "key": "FULLY_SHARDED_LORAS",
+        "input": {
+          "name": "Fully Sharded LoRAs",
+          "type": "boolean",
+          "description": "Enable fully sharded LoRA layers.",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "DEVICE",
+        "input": {
+          "name": "Device",
+          "type": "string",
+          "description": "Device type for vLLM execution.",
+          "options": [
+            {
+              "label": "auto",
+              "value": "auto"
+            },
+            {
+              "label": "cuda",
+              "value": "cuda"
+            },
+            {
+              "label": "neuron",
+              "value": "neuron"
+            },
+            {
+              "label": "cpu",
+              "value": "cpu"
+            },
+            {
+              "label": "openvino",
+              "value": "openvino"
+            },
+            {
+              "label": "tpu",
+              "value": "tpu"
+            },
+            {
+              "label": "xpu",
+              "value": "xpu"
+            }
+          ],
+          "default": "auto",
+          "advanced": true
+        }
+      },
+      {
+        "key": "SCHEDULER_DELAY_FACTOR",
+        "input": {
+          "name": "Scheduler Delay Factor",
+          "type": "number",
+          "description": "Apply a delay before scheduling next prompt.",
+          "default": 0,
+          "advanced": true
+        }
+      },
+      {
+        "key": "ENABLE_CHUNKED_PREFILL",
+        "input": {
+          "name": "Enable Chunked Prefill",
+          "type": "boolean",
+          "description": "Enable chunked prefill requests.",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "SPECULATIVE_CONFIG",
+        "input": {
+          "name": "Speculative Config (JSON)",
+          "type": "string",
+          "description": "Full speculative decoding configuration as a JSON string. Overrides individual speculative env vars.",
+          "advanced": true
+        }
+      },
+      {
+        "key": "SPECULATIVE_METHOD",
+        "input": {
+          "name": "Speculative Method",
+          "type": "string",
+          "description": "Speculative decoding method to use.",
+          "options": [
+            { "label": "None", "value": "" },
+            { "label": "Draft Model", "value": "draft_model" },
+            { "label": "N-gram", "value": "ngram" },
+            { "label": "EAGLE", "value": "eagle" },
+            { "label": "EAGLE3", "value": "eagle3" },
+            { "label": "Medusa", "value": "medusa" },
+            { "label": "MLP Speculator", "value": "mlp_speculator" }
+          ],
+          "default": "",
+          "advanced": true
+        }
+      },
+      {
+        "key": "SPECULATIVE_MODEL",
+        "input": {
+          "name": "Speculative Model",
+          "type": "string",
+          "description": "The name of the draft model to be used in speculative decoding.",
+          "advanced": true
+        }
+      },
+      {
+        "key": "NUM_SPECULATIVE_TOKENS",
+        "input": {
+          "name": "Num Speculative Tokens",
+          "type": "number",
+          "description": "The number of speculative tokens to sample from the draft model.",
+          "advanced": true
+        }
+      },
+      {
+        "key": "NGRAM_PROMPT_LOOKUP_MAX",
+        "input": {
+          "name": "Ngram Prompt Lookup Max",
+          "type": "number",
+          "description": "Max size of window for ngram prompt lookup in speculative decoding.",
+          "advanced": true
+        }
+      },
+      {
+        "key": "MODEL_LOADER_EXTRA_CONFIG",
+        "input": {
+          "name": "Model Loader Extra Config",
+          "type": "string",
+          "description": "Extra config for model loader.",
+          "advanced": true
+        }
+      },
+      {
+        "key": "ENABLE_LOG_REQUESTS",
+        "input": {
+          "name": "Enable Log Requests",
+          "type": "boolean",
+          "description": "Enable vLLM request logging.",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "TOKENIZER_NAME",
+        "input": {
+          "name": "Tokenizer Name",
+          "type": "string",
+          "description": "Tokenizer repo to use a different tokenizer than the model's default",
+          "advanced": true
+        }
+      },
+      {
+        "key": "TOKENIZER_REVISION",
+        "input": {
+          "name": "Tokenizer Revision",
+          "type": "string",
+          "description": "Tokenizer revision to load",
+          "advanced": true
+        }
+      },
+      {
+        "key": "CUSTOM_CHAT_TEMPLATE",
+        "input": {
+          "name": "Custom Chat Template",
+          "type": "string",
+          "description": "Custom chat jinja template",
+          "advanced": true
+        }
+      },
+      {
+        "key": "GPU_MEMORY_UTILIZATION",
+        "input": {
+          "name": "GPU Memory Utilization",
+          "type": "number",
+          "description": "Sets GPU VRAM utilization",
+          "default": 0.95,
+          "advanced": true
+        }
+      },
+      {
+        "key": "BLOCK_SIZE",
+        "input": {
+          "name": "Block Size",
+          "type": "number",
+          "description": "Token block size for contiguous chunks of tokens",
+          "default": 16,
+          "advanced": true
+        }
+      },
+      {
+        "key": "SWAP_SPACE",
+        "input": {
+          "name": "Swap Space",
+          "type": "number",
+          "description": "CPU swap space size (GiB) per GPU",
+          "default": 4,
+          "advanced": true
+        }
+      },
+      {
+        "key": "ENFORCE_EAGER",
+        "input": {
+          "name": "Enforce Eager",
+          "type": "boolean",
+          "description": "Always use eager-mode PyTorch. If False (0), will use eager mode and CUDA graph in hybrid for maximal performance and flexibility",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "DISABLE_CUSTOM_ALL_REDUCE",
+        "input": {
+          "name": "Disable Custom All Reduce",
+          "type": "boolean",
+          "description": "Enables or disables custom all reduce",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "DEFAULT_BATCH_SIZE",
+        "input": {
+          "name": "Default Final Batch Size",
+          "type": "number",
+          "description": "Default and Maximum batch size for token streaming to reduce HTTP calls",
+          "default": 50,
+          "advanced": true
+        }
+      },
+      {
+        "key": "DEFAULT_MIN_BATCH_SIZE",
+        "input": {
+          "name": "Default Starting Batch Size",
+          "type": "number",
+          "description": "Batch size for the first request, which will be multiplied by the growth factor every subsequent request",
+          "default": 1,
+          "advanced": true
+        }
+      },
+      {
+        "key": "DEFAULT_BATCH_SIZE_GROWTH_FACTOR",
+        "input": {
+          "name": "Default Batch Size Growth Factor",
+          "type": "number",
+          "description": "Growth factor for dynamic batch size",
+          "default": 3,
+          "advanced": true
+        }
+      },
+      {
+        "key": "RAW_OPENAI_OUTPUT",
+        "input": {
+          "name": "Raw OpenAI Output",
+          "type": "boolean",
+          "description": "Raw OpenAI output instead of just the text",
+          "default": true,
+          "advanced": true
+        }
+      },
+      {
+        "key": "OPENAI_RESPONSE_ROLE",
+        "input": {
+          "name": "OpenAI Response Role",
+          "type": "string",
+          "description": "Role of the LLM's Response in OpenAI Chat Completions",
+          "default": "assistant",
+          "advanced": true
+        }
+      },
+      {
+        "key": "OPENAI_SERVED_MODEL_NAME_OVERRIDE",
+        "input": {
+          "name": "OpenAI Served Model Name Override",
+          "type": "string",
+          "description": "Overrides the name of the served model from model repo/path to specified name, which you will then be able to use the value for the `model` parameter when making OpenAI requests",
+          "advanced": true
+        }
+      },
+      {
+        "key": "MAX_CONCURRENCY",
+        "input": {
+          "name": "Max Concurrency",
+          "type": "number",
+          "description": "Max concurrent requests per worker. vLLM has an internal queue, so you don't have to worry about limiting by VRAM, this is for improving scaling/load balancing efficiency",
+          "default": 30,
+          "advanced": true
+        }
+      },
+      {
+        "key": "ENABLE_EXPERT_PARALLEL",
+        "input": {
+          "name": "Enable Expert Parallel",
+          "type": "boolean",
+          "description": "Enable Expert Parallel for MoE models",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "MODEL_REVISION",
+        "input": {
+          "name": "Model Revision",
+          "type": "string",
+          "description": "Model revision (branch) to load",
+          "advanced": true
+        }
+      },
+      {
+        "key": "BASE_PATH",
+        "input": {
+          "name": "Base Path",
+          "type": "string",
+          "description": "Storage directory for Huggingface cache and model",
+          "default": "/runpod-volume",
+          "advanced": true
+        }
+      },
+      {
+        "key": "ENABLE_AUTO_TOOL_CHOICE",
+        "input": {
+          "name": "Enable Auto Tool Choice",
+          "type": "boolean",
+          "description": "Enables or disables auto tool choice",
+          "default": false,
+          "advanced": true
+        }
+      },
+      {
+        "key": "TOOL_CALL_PARSER",
+        "input": {
+          "name": "Tool Call Parser",
+          "type": "string",
+          "description": "Tool call parser",
+          "options": [
+            {
+              "label": "None",
+              "value": ""
+            },
+            {
+              "label": "Hermes",
+              "value": "hermes"
+            },
+            {
+              "label": "Mistral",
+              "value": "mistral"
+            },
+            {
+              "label": "Llama3 JSON",
+              "value": "llama3_json"
+            },
+            {
+              "label": "Pythonic",
+              "value": "pythonic"
+            },
+            {
+              "label": "InternLM",
+              "value": "internlm"
+            }
+          ],
+          "default": "",
+          "advanced": true
+        }
+      },
+      {
+        "key": "REASONING_PARSER",
+        "input": {
+          "name": "Reasoning Parser",
+          "type": "string",
+          "description": "Parser for reasoning-capable models (enables reasoning mode)",
+          "options": [
+            { "label": "None", "value": "" },
+            { "label": "DeepSeek R1", "value": "deepseek_r1" },
+            { "label": "Qwen3", "value": "qwen3" },
+            { "label": "Granite", "value": "granite" },
+            { "label": "Hunyuan A13B", "value": "hunyuan_a13b" }
+          ],
+          "default": "",
+          "advanced": true
+        }
+      }
+    ]
+  }
+}
@@ -0,0 +1,43 @@
+{
+  "tests": [
+    {
+      "name": "basic_inference_test",
+      "input": {
+        "prompt": "Write a short poem about artificial intelligence."
+      },
+      "timeout": 30000
+    },
+    {
+      "name": "openai_messages_test",
+      "input": {
+        "openai_route": "/v1/chat/completions",
+        "openai_input": {
+          "messages": [
+            {
+              "role": "system",
+              "content": "You are a helpful assistant that writes concise responses."
+            },
+            {
+              "role": "user",
+              "content": "Explain what a neural network is in one sentence."
+            }
+          ],
+          "max_tokens": 200,
+          "temperature": 0.1
+        }
+      },
+      "timeout": 30000
+    }
+  ],
+  "config": {
+    "gpuTypeId": "NVIDIA GeForce RTX 4090",
+    "gpuCount": 1,
+    "env": [
+      {
+        "key": "MODEL_NAME",
+        "value": "HuggingFaceTB/SmolLM2-135M-Instruct"
+      }
+    ],
+    "allowedCudaVersions": ["12.9", "12.8", "12.7", "12.6", "12.5"]
+  }
+}
@@ -1,49 +1,64 @@
-# Base image - Set default to CUDA 11.8
-ARG WORKER_CUDA_VERSION=11.8
-FROM runpod/base:0.4.2-cuda${WORKER_CUDA_VERSION}.0 as builder
+FROM nvidia/cuda:12.9.1-base-ubuntu22.04 

-ARG WORKER_CUDA_VERSION=11.8 # Required duplicate to keep in scope
+RUN apt-get update -y \
+    && apt-get install -y python3-pip

-# Set Environment Variables
-ENV WORKER_CUDA_VERSION=${WORKER_CUDA_VERSION} \
-    HF_DATASETS_CACHE="/runpod-volume/huggingface-cache/datasets" \
-    HUGGINGFACE_HUB_CACHE="/runpod-volume/huggingface-cache/hub" \
-    TRANSFORMERS_CACHE="/runpod-volume/huggingface-cache/hub" \
-    HF_TRANSFER=1
+RUN ldconfig /usr/local/cuda-12.9/compat/
+
+# Install vLLM with FlashInfer from the CUDA 12.9 wheel index.
+RUN python3 -m pip install --upgrade pip && \
+    python3 -m pip install "vllm[flashinfer]==0.17.0" --extra-index-url https://download.pytorch.org/whl/cu129


-# Install Python dependencies
+
+# Install additional Python dependencies (after vLLM to avoid PyTorch version conflicts)
 COPY builder/requirements.txt /requirements.txt
 RUN --mount=type=cache,target=/root/.cache/pip \
-    python3.11 -m pip install --upgrade pip && \
-    python3.11 -m pip install --upgrade -r /requirements.txt && \
-    rm /requirements.txt
-
-# Install torch and vllm based on CUDA version
-RUN if [[ "${WORKER_CUDA_VERSION}" == 11.8* ]]; then \
-        python3.11 -m pip install -U --force-reinstall torch==2.1.2 xformers==0.0.23.post1 --index-url https://download.pytorch.org/whl/cu118; \
-        python3.11 -m pip install -e git+https://github.com/runpod/vllm-fork-for-sls-worker.git@cuda-11.8#egg=vllm; \
-    else \
-        python3.11 -m pip install -e git+https://github.com/runpod/vllm-fork-for-sls-worker.git#egg=vllm; \
-    fi && \
-    rm -rf /root/.cache/pip
-
-# Add source files
-COPY src .
+    python3 -m pip install --upgrade -r /requirements.txt

 # Setup for Option 2: Building the Image with the Model included
 ARG MODEL_NAME=""
-ARG MODEL_BASE_PATH="/runpod-volume/"
-ARG HF_TOKEN=""
+ARG TOKENIZER_NAME=""
+ARG BASE_PATH="/runpod-volume"
 ARG QUANTIZATION=""
-RUN if [ -n "$MODEL_NAME" ]; then \
-        export MODEL_BASE_PATH=$MODEL_BASE_PATH && \
-        export MODEL_NAME=$MODEL_NAME && \
-        python3.11 /download_model.py --model $MODEL_NAME; \
+ARG MODEL_REVISION=""
+ARG TOKENIZER_REVISION=""
+ARG VLLM_NIGHTLY="false"
+
+ENV MODEL_NAME=$MODEL_NAME \
+    MODEL_REVISION=$MODEL_REVISION \
+    TOKENIZER_NAME=$TOKENIZER_NAME \
+    TOKENIZER_REVISION=$TOKENIZER_REVISION \
+    BASE_PATH=$BASE_PATH \
+    QUANTIZATION=$QUANTIZATION \
+    HF_DATASETS_CACHE="${BASE_PATH}/huggingface-cache/datasets" \
+    HUGGINGFACE_HUB_CACHE="${BASE_PATH}/huggingface-cache/hub" \
+    HF_HOME="${BASE_PATH}/huggingface-cache/hub" \
+    HF_HUB_ENABLE_HF_TRANSFER=0 \
+    # Suppress Ray metrics agent warnings (not needed in containerized environments)
+    RAY_METRICS_EXPORT_ENABLED=0 \
+    RAY_DISABLE_USAGE_STATS=1 \
+    # Prevent rayon thread pool panic in containers where ulimit -u < nproc
+    # (tokenizers uses Rust's rayon which tries to spawn threads = CPU cores)
+    TOKENIZERS_PARALLELISM=false \
+    RAYON_NUM_THREADS=4
+
+ENV PYTHONPATH="/:/vllm-workspace"
+
+RUN if [ "${VLLM_NIGHTLY}" = "true" ]; then \
+    pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly && \
+    apt-get update && apt-get install -y git && rm -rf /var/lib/apt/lists/* && \
+    pip install git+https://github.com/huggingface/transformers.git; \
+fi
+
+COPY src /src
+RUN --mount=type=secret,id=HF_TOKEN,required=false \
+    if [ -f /run/secrets/HF_TOKEN ]; then \
+    export HF_TOKEN=$(cat /run/secrets/HF_TOKEN); \
    fi && \
-    if [ -n "$QUANTIZATION" ]; then \
-        export QUANTIZATION=$QUANTIZATION; \
+    if [ -n "$MODEL_NAME" ]; then \
+    python3 /src/download_model.py; \
    fi

 # Start the handler
-CMD ["python3.11", "/handler.py"]
+CMD ["python3", "/src/handler.py"]
@@ -1,6 +1,6 @@
 MIT License

-Copyright (c) 2023 runpod-workers
+Copyright (c) 2025 Runpod

 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
@@ -1,123 +1,361 @@
 <div align="center">

-<h1>vLLM 0.2.6 Endpoint | Serverless Worker </h1>
+# OpenAI-Compatible vLLM Serverless Endpoint Worker

-[![CD | Docker-Build-Release](https://github.com/runpod-workers/worker-vllm/actions/workflows/docker-build-release.yml/badge.svg)](https://github.com/runpod-workers/worker-vllm/actions/workflows/docker-build-release.yml)
-
-🚀 | This serverless worker utilizes vLLM behind the scenes and is integrated into RunPod's serverless environment. It supports dynamic auto-scaling using the built-in RunPod autoscaling feature.
-</div>
-
-## Setting up the Serverless Worker
-
-### Option 1: Deploy Any Model Using Pre-Built Docker Image
-We now offer a pre-built Docker Image for the vLLM Worker that you can configure entirely with Environment Variables when creating the RunPod Serverless Endpoint:
-
-<div align="center">
-
-```runpod/worker-vllm:dev```
+Deploy OpenAI-Compatible Blazing-Fast LLM Endpoints powered by the [vLLM](https://github.com/vllm-project/vllm) Inference Engine on RunPod Serverless with just a few clicks.

 </div>

-#### Environment Variables
- **Required**:
-   - `MODEL_NAME`: Hugging Face Model Repository (e.g., `openchat/openchat-3.5-1210`).
+## Table of Contents

- **Optional**:
-  - `MAX_MODEL_LENGTH`: Maximum number of tokens for the engine to be able to handle. (default: maximum supported by the model)
-  - `MODEL_BASE_PATH`: Model storage directory (default: `/runpod-volume`).
-  - `HF_TOKEN`: Hugging Face token for private and gated models (e.g., Llama, Falcon).
-  - `NUM_GPU_SHARD`: Number of GPUs to split the model across. (default: `1`)
-  - `QUANTIZATION`: AWQ (`awq`), SqueezeLLM (`squeezellm`) or GPTQ (`gptq`) Quantization. The specified Model Repo must be of a quantized model. (default: `None`)
-  - `TRUST_REMOTE_CODE`: Whether to trust remote code with Hugging Face. (default: `0`)
-  - `MAX_CONCURRENCY`: Max concurrent requests. (default: `100`)
-  - `DEFAULT_BATCH_SIZE`: Token streaming batch size (default: `30`). This reduces the number of HTTP calls, increasing speed 8-10x vs non-batching, matching non-streaming performance.
-  - `DISABLE_LOG_STATS`: Enable (`0`) or disable (`1`) vLLM stats logging.
-  - `DISABLE_LOG_REQUESTS`: Enable (`0`) or disable (`1`) request logging.
+- [Setting up the Serverless Worker](#setting-up-the-serverless-worker)
+  - [Option 1: Deploy Any Model Using Pre-Built Docker Image [Recommended]](#option-1-deploy-any-model-using-pre-built-docker-image-recommended)
+    - [Configuration](#configuration)
+  - [Option 2: Build Docker Image with Model Inside](#option-2-build-docker-image-with-model-inside)
+    - [Prerequisites](#prerequisites)
+    - [Arguments](#arguments)
+    - [Example: Building an image with OpenChat-3.5](#example-building-an-image-with-openchat-35)
+      - [(Optional) Including Huggingface Token](#optional-including-huggingface-token)
+  - [Compatible Model Architectures](#compatible-model-architectures)
+- [Usage: OpenAI Compatibility](#usage-openai-compatibility)
+  - [Modifying your OpenAI Codebase to use your deployed vLLM Worker](#modifying-your-openai-codebase-to-use-your-deployed-vllm-worker)
+  - [OpenAI Request Input Parameters](#openai-request-input-parameters)
+  - [Chat Completions [RECOMMENDED]](#chat-completions-recommended)
+  - [Examples: Using your RunPod endpoint with OpenAI](#examples-using-your-runpod-endpoint-with-openai)
+    - [Chat Completions](#chat-completions)
+    - [Getting a list of names for available models](#getting-a-list-of-names-for-available-models)
+- [Usage: Standard (Non-OpenAI)](#usage-standard-non-openai)
+  - [Request Input Parameters](#request-input-parameters)
+  - [Sampling Parameters](#sampling-parameters)
+    - [Text Input Formats](#text-input-formats)

-### Option 2: Build Docker Image with Model Inside
-[!WARNING] If you are getting errors while building the image, try adding `ENV MAX_JOBS` to the Dockerfile and increase Docker memory limit to at least 25GB.
+# Setting up the Serverless Worker

-To build an image with the model baked in, you must specify the following docker arguments when building the image:
+## Option 1: Deploy Any Model Using Pre-Built Docker Image [Recommended]
+
+**🚀 Deploy Guide**: Follow our [step-by-step deployment guide](https://docs.runpod.io/serverless/vllm/get-started) to deploy using the RunPod Console.
+
+**📦 Docker Image**: `runpod/worker-v1-vllm:<version>`
+
+- **Available Versions**: See [GitHub Releases](https://github.com/runpod-workers/worker-vllm/releases)
+- **CUDA Compatibility**: Requires CUDA >= 12.1
+
+### Configuration
+
+Configure worker-vllm using environment variables:
+
+| Environment Variable                | Description                                       | Default             | Options                                                            |
+| ----------------------------------- | ------------------------------------------------- | ------------------- | ------------------------------------------------------------------ |
+| `MODEL_NAME`                        | Path of the model weights                         | "facebook/opt-125m" | Local folder or Hugging Face repo ID                               |
+| `HF_TOKEN`                          | HuggingFace access token for gated/private models |                     | Your HuggingFace access token                                      |
+| `MAX_MODEL_LEN`                     | Model's maximum context length                    |                     | Integer (e.g., 4096)                                               |
+| `QUANTIZATION`                      | Quantization method                               |                     | "awq", "gptq", "squeezellm", "bitsandbytes"                        |
+| `TENSOR_PARALLEL_SIZE`              | Number of GPUs                                    | 1                   | Integer                                                            |
+| `GPU_MEMORY_UTILIZATION`            | Fraction of GPU memory to use                     | 0.95                | Float between 0.0 and 1.0                                          |
+| `MAX_NUM_SEQS`                      | Maximum number of sequences per iteration         | 256                 | Integer                                                            |
+| `CUSTOM_CHAT_TEMPLATE`              | Custom chat template override                     |                     | Jinja2 template string                                             |
+| `ENABLE_AUTO_TOOL_CHOICE`           | Enable automatic tool selection                   | false               | boolean (true or false)                                            |
+| `TOOL_CALL_PARSER`                  | Parser for tool calls                             |                     | "mistral", "hermes", "llama3_json", "granite", "deepseek_v3", etc. |
+| `OPENAI_SERVED_MODEL_NAME_OVERRIDE` | Override served model name in API                 |                     | String                                                             |
+| `MAX_CONCURRENCY`                   | Maximum concurrent requests                       | 30                  | Integer                                                            |
+
+**Pass any vLLM engine arg** not listed above by setting an environment variable with the **UPPERCASED** field name (same names vLLM uses). The worker auto-discovers all `AsyncEngineArgs` fields from env. For example:
+
+| Environment Variable      | vLLM Engine Arg          | Example Value |
+| ------------------------- | ------------------------ | ------------- |
+| `MAX_MODEL_LEN`           | `max_model_len`          | `4096`        |
+| `ENFORCE_EAGER`           | `enforce_eager`          | `true`        |
+| `ENABLE_CHUNKED_PREFILL`  | `enable_chunked_prefill` | `true`        |
+
+Any env var whose name matches a valid `AsyncEngineArgs` field (uppercased) is applied automatically. Backward-compat aliases: `MODEL_NAME`, `TOKENIZER_NAME`, `MAX_CONTEXT_LEN_TO_CAPTURE`. This lets you configure any vLLM option without waiting for explicit worker support.
+
+For the complete list of all available environment variables, examples, and detailed descriptions: **[Configuration](docs/configuration.md)**
+
+## Option 2: Build Docker Image with Model Inside
+
+To build an image with the model baked in, you must specify the following docker arguments when building the image.
+
+### Prerequisites
+
+- Docker
+
+### Arguments

-#### Arguments:
 - **Required**
  - `MODEL_NAME`
 - **Optional**
-  - `MODEL_BASE_PATH`: Defaults to `/runpod-volume` for network storage. Use `/models` or for local container storage.
+  - `MODEL_REVISION`: Model revision to load (default: `main`).
+  - `BASE_PATH`: Storage directory where huggingface cache and model will be located. (default: `/runpod-volume`, which will utilize network storage if you attach it or create a local directory within the image if you don't. If your intention is to bake the model into the image, you should set this to something like `/models` to make sure there are no issues if you were to accidentally attach network storage.)
  - `QUANTIZATION`
-  - `HF_TOKEN`
-  - `WORKER_CUDA_VERSION`: `11.8` or `12.1` (default: `11.8` due to a small amount of workers not having CUDA 12.1 support yet. `12.1` is recommended for optimal performance).
+  - `WORKER_CUDA_VERSION`: `12.1.0` (`12.1.0` is recommended for optimal performance).
+  - `TOKENIZER_NAME`: Tokenizer repository if you would like to use a different tokenizer than the one that comes with the model. (default: `None`, which uses the model's tokenizer)
+  - `TOKENIZER_REVISION`: Tokenizer revision to load (default: `main`).
+  - `VLLM_NIGHTLY`: Set to `true` to replace the pinned vLLM release with the latest nightly build and the latest `transformers` from source. Useful for testing unreleased vLLM features. (default: `false`)

-#### Example: Building an image with OpenChat-3.5
-`sudo docker build -t username/image:tag --build-arg MODEL_NAME="openchat/openchat_3.5" --build-arg MODEL_BASE_PATH="/models" .`
+For the remaining settings, you may apply them as environment variables when running the container. Supported environment variables are listed in the [Environment Variables](#environment-variables) section.

-### Compatible Models
- LLaMA & LLaMA-2 (`meta-llama/Llama-2-70b-hf`, `lmsys/vicuna-13b-v1.3`, `young-geng/koala`, `openlm-research/open_llama_13b`, etc.)
- Mistral (`mistralai/Mistral-7B-v0.1`, `mistralai/Mistral-7B-Instruct-v0.1`, etc.)
- Mixtral (`mistralai/Mixtral-8x7B-v0.1`, `mistralai/Mixtral-8x7B-Instruct-v0.1`, etc.)
- Aquila & Aquila2 (`BAAI/AquilaChat2-7B`, `BAAI/AquilaChat2-34B`, `BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc.)
- Baichuan & Baichuan2 (`baichuan-inc/Baichuan2-13B-Chat`, `baichuan-inc/Baichuan-7B`, etc.)
- BLOOM (`bigscience/bloom`, `bigscience/bloomz`, etc.)
- ChatGLM (`THUDM/chatglm2-6b`, `THUDM/chatglm3-6b`, etc.)
- Falcon (`tiiuae/falcon-7b`, `tiiuae/falcon-40b`, `tiiuae/falcon-rw-7b`, etc.)
- GPT-2 (`gpt2`, `gpt2-xl`, etc.)
- GPT BigCode (`bigcode/starcoder`, `bigcode/gpt_bigcode-santacoder`, etc.)
- GPT-J (`EleutherAI/gpt-j-6b`, `nomic-ai/gpt4all-j`, etc.)
- GPT-NeoX (`EleutherAI/gpt-neox-20b`, `databricks/dolly-v2-12b`, `stabilityai/stablelm-tuned-alpha-7b`, etc.)
- InternLM (`internlm/internlm-7b`, `internlm/internlm-chat-7b`, etc.)
- MPT (`mosaicml/mpt-7b`, `mosaicml/mpt-30b`, etc.)
- OPT (`facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc.)
- Phi (`microsoft/phi-1_5`, `microsoft/phi-2`, etc.)
- Qwen (`Qwen/Qwen-7B`, `Qwen/Qwen-7B-Chat`, etc.)
- Yi (`01-ai/Yi-6B`, `01-ai/Yi-34B`, etc.)
-  
-And any other models supported by vLLM 0.2.6.
+### Example: Building an image with OpenChat-3.5

-
-Ensure that you have Docker installed and properly set up before running the docker build commands. Once built, you can deploy this serverless worker in your desired environment with confidence that it will automatically scale based on demand. For further inquiries or assistance, feel free to contact our support team.
-
-
-## Model Inputs
-You may either use a `prompt` or a list of `messages` as input. If you use `messages`, the model's chat template will be applied to the messages automatically, so the model must have one. If you use `prompt`, you may optionally apply the model's chat template to the prompt by setting `apply_chat_template` to `true`.
-| Argument        | Type | Default            | Description                                                                                   |
-|-----------------|------|--------------------|-----------------------------------------------------------------------------------------------|
-| `prompt`          | str  |                    | Prompt string to generate text based on.                                                      |
-| `messages`          | list[dict[str, str]]  |                    | List of messages, which will automatically have the model's chat template applied. Overrides `prompt`.                                                 |
-| `apply_chat_template`       | bool | False              | Whether to apply the model's chat template to the `prompt`. |
-| `sampling_params` | dict | {}                 | Sampling parameters to control the generation, like temperature, top_p, etc.                  |
-| `stream`       | bool | False              | Whether to enable streaming of output. If True, responses are streamed as they are generated. |
-| `batch_size`      | int  | DEFAULT_BATCH_SIZE | The number of tokens to stream every HTTP POST call.                            |
-
-### Messages Format
-Your list can contain any number of messages, and each message can have any role from the following list:
- `user`
- `assistant`
- `system`
-
-The model's chat template will be applied to the messages automatically. 
-
-Example:
-```json
-[
-  {
-    "role": "system",
-    "content": "..."
-  },
-  {
-    "role": "user",
-    "content": "..."
-  },
-  {
-    "role": "assistant",
-    "content": "..."
-  }
-]
+```bash
+docker build -t username/image:tag --build-arg MODEL_NAME="openchat/openchat_3.5" --build-arg BASE_PATH="/models" .
 ```

+### Example: Building with vLLM Nightly
+
+To use the latest unreleased vLLM build (installs from the nightly wheel index and `transformers` from source):
+
+```bash
+docker build -t username/image:tag --build-arg VLLM_NIGHTLY=true .
+```
+
+You can combine it with other arguments:
+
+```bash
+docker build -t username/image:tag --build-arg VLLM_NIGHTLY=true --build-arg MODEL_NAME="meta-llama/Llama-3.1-8B-Instruct" --build-arg BASE_PATH="/models" .
+```
+
+### (Optional) Including Huggingface Token
+
+If the model you would like to deploy is private or gated, you will need to include it during build time as a Docker secret, which will protect it from being exposed in the image and on DockerHub.
+
+1. Enable Docker BuildKit (required for secrets).
+
+```bash
+export DOCKER_BUILDKIT=1
+```
+
+2. Export your Hugging Face token as an environment variable
+
+```bash
+export HF_TOKEN="your_token_here"
+```
+
+2. Add the token as a secret when building
+
+```bash
+docker build -t username/image:tag --secret id=HF_TOKEN --build-arg MODEL_NAME="openchat/openchat_3.5" .
+```
+
+# Compatible Model Architectures
+
+You can deploy **any model on Hugging Face** that is supported by vLLM. For the complete and up-to-date list of supported model architectures, see the [vLLM Supported Models documentation](https://docs.vllm.ai/en/latest/models/supported_models.html#list-of-text-only-language-models).
+
+# Usage: OpenAI Compatibility
+
+The vLLM Worker is fully compatible with OpenAI's API, and you can use it with any OpenAI Codebase by changing only 3 lines in total. The supported routes are <ins>Chat Completions</ins> and <ins>Models</ins> - with both streaming and non-streaming.
+
+## Modifying your OpenAI Codebase to use your deployed vLLM Worker
+
+**Python** (similar to Node.js, etc.):
+
+1. When initializing the OpenAI Client in your code, change the `api_key` to your RunPod API Key and the `base_url` to your RunPod Serverless Endpoint URL in the following format: `https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1`, filling in your deployed endpoint ID. For example, if your Endpoint ID is `abc1234`, the URL would be `https://api.runpod.ai/v2/abc1234/openai/v1`.
+
+   - Before:
+
+   ```python
+   from openai import OpenAI
+
+   client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+   ```
+
+   - After:
+
+   ```python
+   from openai import OpenAI
+
+   client = OpenAI(
+       api_key=os.environ.get("RUNPOD_API_KEY"),
+       base_url="https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1",
+   )
+   ```
+
+2. Change the `model` parameter to your deployed model's name whenever using Completions or Chat Completions.
+   - Before:
+   ```python
+   response = client.chat.completions.create(
+       model="gpt-3.5-turbo",
+       messages=[{"role": "user", "content": "Why is RunPod the best platform?"}],
+       temperature=0,
+       max_tokens=100,
+   )
+   ```
+   - After:
+   ```python
+   response = client.chat.completions.create(
+       model="<YOUR DEPLOYED MODEL REPO/NAME>",
+       messages=[{"role": "user", "content": "Why is RunPod the best platform?"}],
+       temperature=0,
+       max_tokens=100,
+   )
+   ```
+
+**Using http requests**:
+
+1. Change the `Authorization` header to your RunPod API Key and the `url` to your RunPod Serverless Endpoint URL in the following format: `https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1`
+   - Before:
+   ```bash
+   curl https://api.openai.com/v1/chat/completions \
+   -H "Content-Type: application/json" \
+   -H "Authorization: Bearer $OPENAI_API_KEY" \
+   -d '{
+   "model": "gpt-4",
+   "messages": [
+     {
+       "role": "user",
+       "content": "Why is RunPod the best platform?"
+     }
+   ],
+   "temperature": 0,
+   "max_tokens": 100
+   }'
+   ```
+   - After:
+   ```bash
+   curl https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1/chat/completions \
+   -H "Content-Type: application/json" \
+   -H "Authorization: Bearer <YOUR OPENAI API KEY>" \
+   -d '{
+   "model": "<YOUR DEPLOYED MODEL REPO/NAME>",
+   "messages": [
+     {
+       "role": "user",
+       "content": "Why is RunPod the best platform?"
+     }
+   ],
+   "temperature": 0,
+   "max_tokens": 100
+   }'
+   ```
+
+## OpenAI Request Input Parameters:
+
+When using the chat completion feature of the vLLM Serverless Endpoint Worker, you can customize your requests with the following parameters:
+
+### Chat Completions [RECOMMENDED]
+
+<details>
+  <summary>Supported Chat Completions Inputs and Descriptions</summary>
+
+| Parameter           | Type                             | Default Value | Description                                                                                                                                                                                                                                                  |
+| ------------------- | -------------------------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `messages`          | Union[str, List[Dict[str, str]]] |               | List of messages, where each message is a dictionary with a `role` and `content`. The model's chat template will be applied to the messages automatically, so the model must have one or it should be specified as `CUSTOM_CHAT_TEMPLATE` env var.           |
+| `model`             | str                              |               | The model repo that you've deployed on your RunPod Serverless Endpoint. If you are unsure what the name is or are baking the model in, use the guide to get the list of available models in the **Examples: Using your RunPod endpoint with OpenAI** section |
+| `temperature`       | Optional[float]                  | 0.7           | Float that controls the randomness of the sampling. Lower values make the model more deterministic, while higher values make the model more random. Zero means greedy sampling.                                                                              |
+| `top_p`             | Optional[float]                  | 1.0           | Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.                                                                                                                            |
+| `n`                 | Optional[int]                    | 1             | Number of output sequences to return for the given prompt.                                                                                                                                                                                                   |
+| `max_tokens`        | Optional[int]                    | None          | Maximum number of tokens to generate per output sequence.                                                                                                                                                                                                    |
+| `seed`              | Optional[int]                    | None          | Random seed to use for the generation.                                                                                                                                                                                                                       |
+| `stop`              | Optional[Union[str, List[str]]]  | list          | List of strings that stop the generation when they are generated. The returned output will not contain the stop strings.                                                                                                                                     |
+| `stream`            | Optional[bool]                   | False         | Whether to stream or not                                                                                                                                                                                                                                     |
+| `presence_penalty`  | Optional[float]                  | 0.0           | Float that penalizes new tokens based on whether they appear in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.                                                          |
+| `frequency_penalty` | Optional[float]                  | 0.0           | Float that penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.                                                              |
+| `logit_bias`        | Optional[Dict[str, float]]       | None          | Unsupported by vLLM                                                                                                                                                                                                                                          |
+| `user`              | Optional[str]                    | None          | Unsupported by vLLM                                                                                                                                                                                                                                          |
+
+Additional parameters supported by vLLM:
+| `best_of` | Optional[int] | None | Number of output sequences that are generated from the prompt. From these `best_of` sequences, the top `n` sequences are returned. `best_of` must be greater than or equal to `n`. This is treated as the beam width when `use_beam_search` is True. By default, `best_of` is set to `n`. |
+| `top_k` | Optional[int] | -1 | Integer that controls the number of top tokens to consider. Set to -1 to consider all tokens. |
+| `ignore_eos` | Optional[bool] | False | Whether to ignore the EOS token and continue generating tokens after the EOS token is generated. |
+| `use_beam_search` | Optional[bool] | False | Whether to use beam search instead of sampling. |
+| `stop_token_ids` | Optional[List[int]] | list | List of tokens that stop the generation when they are generated. The returned output will contain the stop tokens unless the stop tokens are special tokens. |
+| `skip_special_tokens` | Optional[bool] | True | Whether to skip special tokens in the output. |
+| `spaces_between_special_tokens`| Optional[bool] | True | Whether to add spaces between special tokens in the output. Defaults to True. |
+| `add_generation_prompt` | Optional[bool] | True | Read more [here](https://huggingface.co/docs/transformers/main/en/chat_templating#what-are-generation-prompts) |
+| `echo` | Optional[bool] | False | Echo back the prompt in addition to the completion |
+| `repetition_penalty` | Optional[float] | 1.0 | Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far. Values > 1 encourage the model to use new tokens, while values < 1 encourage the model to repeat tokens. |
+| `min_p` | Optional[float] | 0.0 | Float that represents the minimum probability for a token to |
+| `length_penalty` | Optional[float] | 1.0 | Float that penalizes sequences based on their length. Used in beam search.. |
+| `include_stop_str_in_output` | Optional[bool] | False | Whether to include the stop strings in output text. Defaults to False.|
+
+</details>
+
+### Examples: Using your RunPod endpoint with OpenAI
+
+First, initialize the OpenAI Client with your RunPod API Key and Endpoint URL:
+
+```python
+from openai import OpenAI
+import os
+
+# Initialize the OpenAI Client with your RunPod API Key and Endpoint URL
+client = OpenAI(
+    api_key=os.environ.get("RUNPOD_API_KEY"),
+    base_url="https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1",
+)
+```
+
+### Chat Completions:
+
+This is the format used for GPT-4 and focused on instruction-following and chat. Examples of Open Source chat/instruct models include `meta-llama/Llama-2-7b-chat-hf`, `mistralai/Mixtral-8x7B-Instruct-v0.1`, `openchat/openchat-3.5-0106`, `NousResearch/Nous-Hermes-2-Mistral-7B-DPO` and more. However, if your model is a completion-style model with no chat/instruct fine-tune and/or does not have a chat template, you can still use this if you provide a chat template with the environment variable `CUSTOM_CHAT_TEMPLATE`.
+
+- **Streaming**:
+  ```python
+  # Create a chat completion stream
+  response_stream = client.chat.completions.create(
+      model="<YOUR DEPLOYED MODEL REPO/NAME>",
+      messages=[{"role": "user", "content": "Why is RunPod the best platform?"}],
+      temperature=0,
+      max_tokens=100,
+      stream=True,
+  )
+  # Stream the response
+  for response in response_stream:
+      print(chunk.choices[0].delta.content or "", end="", flush=True)
+  ```
+- **Non-Streaming**:
+  ```python
+  # Create a chat completion
+  response = client.chat.completions.create(
+      model="<YOUR DEPLOYED MODEL REPO/NAME>",
+      messages=[{"role": "user", "content": "Why is RunPod the best platform?"}],
+      temperature=0,
+      max_tokens=100,
+  )
+  # Print the response
+  print(response.choices[0].message.content)
+  ```
+
+### Getting a list of names for available models:
+
+In the case of baking the model into the image, sometimes the repo may not be accepted as the `model` in the request. In this case, you can list the available models as shown below and use that name.
+
+```python
+models_response = client.models.list()
+list_of_models = [model.id for model in models_response]
+print(list_of_models)
+```
+
+# Usage: Standard (Non-OpenAI)
+
+## Request Input Parameters
+
+<details>
+  <summary>Click to expand table</summary>
+    
+  You may either use a `prompt` or a list of `messages` as input. If you use `messages`, the model's chat template will be applied to the messages automatically, so the model must have one. If you use `prompt`, you may optionally apply the model's chat template to the prompt by setting `apply_chat_template` to `true`.
+  | Argument              | Type                 | Default            | Description                                                                                            |
+  |-----------------------|----------------------|--------------------|--------------------------------------------------------------------------------------------------------|
+  | `prompt`              | str                  |                    | Prompt string to generate text based on.                                                               |
+  | `messages`            | list[dict[str, str]] |                    | List of messages, which will automatically have the model's chat template applied. Overrides `prompt`. |
+  | `apply_chat_template` | bool                 | False              | Whether to apply the model's chat template to the `prompt`.                                            |
+  | `sampling_params`     | dict                 | {}                 | Sampling parameters to control the generation, like temperature, top_p, etc. You can find all available parameters in the `Sampling Parameters` section below. |
+  | `stream`              | bool                 | False              | Whether to enable streaming of output. If True, responses are streamed as they are generated.          |
+  | `max_batch_size`          | int                  | env var `DEFAULT_BATCH_SIZE` | The maximum number of tokens to stream every HTTP POST call.                                                   |
+  | `min_batch_size`          | int                  | env var `DEFAULT_MIN_BATCH_SIZE` | The minimum number of tokens to stream every HTTP POST call.                                           |
+  | `batch_size_growth_factor` | int                  | env var `DEFAULT_BATCH_SIZE_GROWTH_FACTOR` | The growth factor by which `min_batch_size` will be multiplied for each call until `max_batch_size` is reached.           |
+</details>
+
 ### Sampling Parameters
-| Argument                      | Type                        | Default | Description                                                                                                                                                                                   |
-|-------------------------------|-----------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| `best_of`                       | Optional[int]               | None    | Number of output sequences generated from the prompt. The top `n` sequences are returned from these `best_of` sequences. Must be ≥ `n`. Treated as beam width in beam search. Default is `n`. |
+
+Below are all available sampling parameters that you can specify in the `sampling_params` dictionary. If you do not specify any of these parameters, the default values will be used.
+
+<details>
+  <summary>Click to expand table</summary>
+
+| Argument                        | Type                        | Default | Description                                                                                                                                                                                   |
+| ------------------------------- | --------------------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `n`                             | int                         | 1       | Number of output sequences generated from the prompt. The top `n` sequences are returned.                                                                                                     |
+| `best_of`                       | Optional[int]               | `n`     | Number of output sequences generated from the prompt. The top `n` sequences are returned from these `best_of` sequences. Must be ≥ `n`. Treated as beam width in beam search. Default is `n`. |
 | `presence_penalty`              | float                       | 0.0     | Penalizes new tokens based on their presence in the generated text so far. Values > 0 encourage new tokens, values < 0 encourage repetition.                                                  |
 | `frequency_penalty`             | float                       | 0.0     | Penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage new tokens, values < 0 encourage repetition.                                                 |
 | `repetition_penalty`            | float                       | 1.0     | Penalizes new tokens based on their appearance in the prompt and generated text. Values > 1 encourage new tokens, values < 1 encourage repetition.                                            |
@@ -128,10 +366,65 @@ Example:
 | `use_beam_search`               | bool                        | False   | Whether to use beam search instead of sampling.                                                                                                                                               |
 | `length_penalty`                | float                       | 1.0     | Penalizes sequences based on their length. Used in beam search.                                                                                                                               |
 | `early_stopping`                | Union[bool, str]            | False   | Controls stopping condition in beam search. Can be `True`, `False`, or `"never"`.                                                                                                             |
-| `stop`                          | Union[None, str, List[str]] | None    | List of strings that stop generation when produced. Output will not contain these strings.                                                                                                    |
+| `stop`                          | Union[None, str, List[str]] | None    | List of strings that stop generation when produced. The output will not contain these strings.                                                                                                |
 | `stop_token_ids`                | Optional[List[int]]         | None    | List of token IDs that stop generation when produced. Output contains these tokens unless they are special tokens.                                                                            |
 | `ignore_eos`                    | bool                        | False   | Whether to ignore the End-Of-Sequence token and continue generating tokens after its generation.                                                                                              |
 | `max_tokens`                    | int                         | 16      | Maximum number of tokens to generate per output sequence.                                                                                                                                     |
 | `skip_special_tokens`           | bool                        | True    | Whether to skip special tokens in the output.                                                                                                                                                 |
 | `spaces_between_special_tokens` | bool                        | True    | Whether to add spaces between special tokens in the output.                                                                                                                                   |

+### Text Input Formats
+
+You may either use a `prompt` or a list of `messages` as input.
+
+1.  `prompt`
+    The prompt string can be any string, and the model's chat template will not be applied to it unless `apply_chat_template` is set to `true`, in which case it will be treated as a user message.
+
+        Example:
+        ```json
+        {
+          "input": {
+            "prompt": "why sky is blue?",
+            "sampling_params": {
+              "temperature": 0.7,
+              "max_tokens": 100
+            }
+          }
+        }
+        ```
+
+2.  `messages`
+    Your list can contain any number of messages, and each message usually can have any role from the following list: - `user` - `assistant` - `system`
+
+    However, some models may have different roles, so you should check the model's chat template to see which roles are required.
+
+    The model's chat template will be applied to the messages automatically, so the model must have one.
+
+    Example:
+
+    ```json
+    {
+      "input": {
+        "messages": [
+          {
+            "role": "system",
+            "content": "You are a helpful AI assistant that provides clear and concise responses."
+          },
+          {
+            "role": "user",
+            "content": "Can you explain the difference between supervised and unsupervised learning?"
+          },
+          {
+            "role": "assistant",
+            "content": "Sure! Supervised learning uses labeled data, meaning each input has a corresponding correct output. The model learns by mapping inputs to known outputs. In contrast, unsupervised learning works with unlabeled data, where the model identifies patterns, structures, or clusters without predefined answers."
+          }
+        ],
+        "sampling_params": {
+          "temperature": 0.7,
+          "max_tokens": 100
+        }
+      }
+    }
+    ```
+
+</details>
@@ -1,6 +1,14 @@
-hf_transfer
-runpod==1.5.2
+ray
+pandas
+pyarrow
+runpod
 huggingface-hub
 packaging
-typing-extensions==4.7.1
+typing-extensions>=4.8.0
 pydantic
+pydantic-settings
+hf-transfer
+transformers>=4.57.0
+bitsandbytes>=0.45.0
+kernels
+torch-c-dlpack-ext
@@ -0,0 +1,26 @@
+variable "DOCKERHUB_REPO" {
+  default = "runpod"
+}
+
+variable "DOCKERHUB_IMG" {
+  default = "worker-v1-vllm"
+}
+
+variable "RELEASE_VERSION" {
+  default = "latest"
+}
+
+variable "HUGGINGFACE_ACCESS_TOKEN" {
+  default = ""
+}
+
+group "default" {
+  targets = ["worker-vllm"]
+}
+
+target "worker-vllm" {
+  tags = ["${DOCKERHUB_REPO}/${DOCKERHUB_IMG}:${RELEASE_VERSION}"]
+  context = "."
+  dockerfile = "Dockerfile"
+  platforms = ["linux/amd64"]
+}
@@ -0,0 +1,202 @@
+# Configuration Reference
+
+Complete guide to all environment variables and configuration options for worker-vllm.
+
+## LLM Settings
+
+| Variable                       | Default             | Type/Choices                                                | Description                                                                     |
+| ------------------------------ | ------------------- | ----------------------------------------------------------- | ------------------------------------------------------------------------------- |
+| `MODEL_NAME`                   | 'facebook/opt-125m' | `str`                                                       | Name or path of the Hugging Face model to use.                                  |
+| `MODEL_REVISION`               | 'main'              | `str`                                                       | Model revision to load (default: main).                                         |
+| `TOKENIZER`                    | None                | `str`                                                       | Name or path of the Hugging Face tokenizer to use.                              |
+| `SKIP_TOKENIZER_INIT`          | False               | `bool`                                                      | Skip initialization of tokenizer and detokenizer.                               |
+| `TOKENIZER_MODE`               | 'auto'              | ['auto', 'slow']                                            | The tokenizer mode.                                                             |
+| `TRUST_REMOTE_CODE`            | `False`             | `bool`                                                      | Trust remote code from Hugging Face.                                            |
+| `DOWNLOAD_DIR`                 | None                | `str`                                                       | Directory to download and load the weights.                                     |
+| `LOAD_FORMAT`                  | 'auto'              | `str`                                                       | The format of the model weights to load.                                        |
+| `HF_TOKEN`                     | -                   | `str`                                                       | Hugging Face token for private and gated models.                                |
+| `DTYPE`                        | 'auto'              | ['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'] | Data type for model weights and activations.                                    |
+| `KV_CACHE_DTYPE`               | 'auto'              | ['auto', 'fp8']                                             | Data type for KV cache storage.                                                 |
+| `QUANTIZATION_PARAM_PATH`      | None                | `str`                                                       | Path to the JSON file containing the KV cache scaling factors.                  |
+| `MAX_MODEL_LEN`                | None                | `int`                                                       | Model context length.                                                           |
+| `GUIDED_DECODING_BACKEND`      | 'outlines'          | ['outlines', 'lm-format-enforcer']                          | Which engine will be used for guided decoding by default.                       |
+| `DISTRIBUTED_EXECUTOR_BACKEND` | None                | ['ray', 'mp']                                               | Backend to use for distributed serving.                                         |
+| `WORKER_USE_RAY`               | False               | `bool`                                                      | Deprecated, use --distributed-executor-backend=ray.                             |
+| `PIPELINE_PARALLEL_SIZE`       | 1                   | `int`                                                       | Number of pipeline stages.                                                      |
+| `TENSOR_PARALLEL_SIZE`         | 1                   | `int`                                                       | Number of tensor parallel replicas.                                             |
+| `MAX_PARALLEL_LOADING_WORKERS` | None                | `int`                                                       | Load model sequentially in multiple batches.                                    |
+| `RAY_WORKERS_USE_NSIGHT`       | False               | `bool`                                                      | If specified, use nsight to profile Ray workers.                                |
+| `ENABLE_PREFIX_CACHING`        | False               | `bool`                                                      | Enables automatic prefix caching.                                               |
+| `DISABLE_SLIDING_WINDOW`       | False               | `bool`                                                      | Disables sliding window, capping to sliding window size.                        |
+| `NUM_LOOKAHEAD_SLOTS`          | 0                   | `int`                                                       | Experimental scheduling config necessary for speculative decoding.              |
+| `SEED`                         | 0                   | `int`                                                       | Random seed for operations.                                                     |
+| `NUM_GPU_BLOCKS_OVERRIDE`      | None                | `int`                                                       | If specified, ignore GPU profiling result and use this number of GPU blocks.    |
+| `MAX_NUM_BATCHED_TOKENS`       | None                | `int`                                                       | Maximum number of batched tokens per iteration.                                 |
+| `MAX_NUM_SEQS`                 | 256                 | `int`                                                       | Maximum number of sequences per iteration.                                      |
+| `MAX_LOGPROBS`                 | 20                  | `int`                                                       | Max number of log probs to return when logprobs is specified in SamplingParams. |
+| `DISABLE_LOG_STATS`            | False               | `bool`                                                      | Disable logging statistics.                                                     |
+| `QUANTIZATION`                 | None                | ['awq', 'squeezellm', 'gptq', 'bitsandbytes']               | Method used to quantize the weights.                                            |
+| `ROPE_SCALING`                 | None                | `dict`                                                      | RoPE scaling configuration in JSON format.                                      |
+| `ROPE_THETA`                   | None                | `float`                                                     | RoPE theta. Use with rope_scaling.                                              |
+| `TOKENIZER_POOL_SIZE`          | 0                   | `int`                                                       | Size of tokenizer pool to use for asynchronous tokenization.                    |
+| `TOKENIZER_POOL_TYPE`          | 'ray'               | `str`                                                       | Type of tokenizer pool to use for asynchronous tokenization.                    |
+| `TOKENIZER_POOL_EXTRA_CONFIG`  | None                | `dict`                                                      | Extra config for tokenizer pool.                                                |
+
+## LoRA (Low-Rank Adaptation) Settings
+
+| Variable                    | Default | Type                                       | Description                                                                                               |
+| --------------------------- | ------- | ------------------------------------------ | --------------------------------------------------------------------------------------------------------- |
+| `ENABLE_LORA`               | False   | `bool`                                     | If True, enable handling of LoRA adapters.                                                                |
+| `MAX_LORAS`                 | 1       | `int`                                      | Max number of LoRAs in a single batch.                                                                    |
+| `MAX_LORA_RANK`             | 16      | `int`                                      | Max LoRA rank.                                                                                            |
+| `LORA_EXTRA_VOCAB_SIZE`     | 256     | `int`                                      | Maximum size of extra vocabulary for LoRA adapters.                                                       |
+| `LORA_DTYPE`                | 'auto'  | ['auto', 'float16', 'bfloat16', 'float32'] | Data type for LoRA.                                                                                       |
+| `LONG_LORA_SCALING_FACTORS` | None    | `tuple`                                    | Specify multiple scaling factors for LoRA adapters.                                                       |
+| `MAX_CPU_LORAS`             | None    | `int`                                      | Maximum number of LoRAs to store in CPU memory.                                                           |
+| `FULLY_SHARDED_LORAS`       | False   | `bool`                                     | Enable fully sharded LoRA layers.                                                                         |
+| `LORA_MODULES`              | `[]`    | `list[dict]`                               | Add lora adapters from Hugging Face `[{"name": "xx", "path": "xxx/xxxx", "base_model_name": "xxx/xxxx"}]` |
+
+> **Note (Serverless)**: When LoRA adapters are configured via `LORA_MODULES`, initialization is deferred to the first request to ensure compatibility with RunPod Serverless. This means the first request will include LoRA loading time. Subsequent requests are unaffected. Check logs for "LoRA mode: X adapter(s) will load on first request" at startup.
+
+## Speculative Decoding Settings
+
+Speculative decoding can be configured in two ways:
+
+### Option 1: JSON Configuration
+
+Set `SPECULATIVE_CONFIG` to a JSON string with your full speculative decoding configuration:
+
+```bash
+SPECULATIVE_CONFIG='{"method": "ngram", "num_speculative_tokens": 5, "prompt_lookup_max": 4}'
+```
+
+### Option 2: Individual Environment Variables
+
+| Variable                                 | Default | Type/Choices                                                       | Description                                                                               |
+| ---------------------------------------- | ------- | ------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- |
+| `SPECULATIVE_METHOD`                     | None    | ['draft_model', 'ngram', 'eagle', 'eagle3', 'medusa', 'mlp_speculator'] | Speculative decoding method to use.                                                       |
+| `SPECULATIVE_MODEL`                      | None    | `str`                                                              | The name of the draft model to be used in speculative decoding.                           |
+| `NUM_SPECULATIVE_TOKENS`                 | None    | `int`                                                              | The number of speculative tokens to sample from the draft model.                          |
+| `SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE` | None    | `int`                                                              | Number of tensor parallel replicas for the draft model.                                   |
+| `SPECULATIVE_MAX_MODEL_LEN`              | None    | `int`                                                              | The maximum sequence length supported by the draft model.                                 |
+| `SPECULATIVE_DISABLE_BY_BATCH_SIZE`      | None    | `int`                                                              | Disable speculative decoding if the number of enqueue requests is larger than this value. |
+| `NGRAM_PROMPT_LOOKUP_MAX`                | None    | `int`                                                              | Max size of window for ngram prompt lookup in speculative decoding.                       |
+| `NGRAM_PROMPT_LOOKUP_MIN`                | None    | `int`                                                              | Min size of window for ngram prompt lookup in speculative decoding.                       |
+
+If `SPECULATIVE_CONFIG` is set, it takes priority over individual env vars. When using individual env vars without `SPECULATIVE_METHOD`, the method is auto-detected from the model name or configuration.
+
+## Scheduling & Performance Settings
+
+| Variable                       | Default | Type/Choices    | Description                                                                                                                         |
+| ------------------------------ | ------- | --------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
+| `GPU_MEMORY_UTILIZATION`       | `0.95`  | `float`         | Sets GPU VRAM utilization.                                                                                                          |
+| `MAX_PARALLEL_LOADING_WORKERS` | `None`  | `int`           | Load model sequentially in multiple batches, to avoid RAM OOM when using tensor parallel and large models.                          |
+| `BLOCK_SIZE`                   | `16`    | `8`, `16`, `32` | Token block size for contiguous chunks of tokens.                                                                                   |
+| `SWAP_SPACE`                   | `4`     | `int`           | CPU swap space size (GiB) per GPU.                                                                                                  |
+| `ENFORCE_EAGER`                | False   | `bool`          | Always use eager-mode PyTorch. If False(`0`), will use eager mode and CUDA graph in hybrid for maximal performance and flexibility. |
+| `MAX_SEQ_LEN_TO_CAPTURE`       | `8192`  | `int`           | Maximum context length covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode.     |
+| `DISABLE_CUSTOM_ALL_REDUCE`    | `0`     | `int`           | Enables or disables custom all reduce.                                                                                              |
+| `ENABLE_EXPERT_PARALLEL`       | `False` | `bool`          | Enable Expert Parallel for MoE models.                                                                                              |
+| `ATTENTION_BACKEND`            | `None`  | `str`           | Attention backend to use (e.g., `FLASH_ATTN`, `FLASHINFER`, `TRITON_FLASH_ATTN`). Replaces deprecated `VLLM_ATTENTION_BACKEND`.     |
+| `ASYNC_SCHEDULING`             | `None`  | `bool`          | Enable async scheduling (overlaps engine scheduling with GPU execution). Default: enabled in vLLM 0.14.0+. Set to `false` to disable. |
+| `STREAM_INTERVAL`              | `1`     | `int`           | Controls how often to yield streaming results. Lower = more frequent updates.                                                        |
+
+## Tokenizer Settings
+
+| Variable               | Default | Type/Choices                        | Description                                                                                       |
+| ---------------------- | ------- | ----------------------------------- | ------------------------------------------------------------------------------------------------- |
+| `TOKENIZER_NAME`       | `None`  | `str`                               | Tokenizer repository to use a different tokenizer than the model's default.                       |
+| `TOKENIZER_REVISION`   | `None`  | `str`                               | Tokenizer revision to load.                                                                       |
+| `CUSTOM_CHAT_TEMPLATE` | `None`  | `str` of single-line jinja template | Custom chat jinja template. [More Info](https://huggingface.co/docs/transformers/chat_templating) |
+
+## Streaming & Batch Settings
+
+The way this works is that the first request will have a batch size of `DEFAULT_MIN_BATCH_SIZE`, and each subsequent request will have a batch size of `previous_batch_size * DEFAULT_BATCH_SIZE_GROWTH_FACTOR`. This will continue until the batch size reaches `DEFAULT_BATCH_SIZE`. E.g. for the default values, the batch sizes will be `1, 3, 9, 27, 50, 50, 50, ...`. You can also specify this per request, with inputs `max_batch_size`, `min_batch_size`, and `batch_size_growth_factor`. This has nothing to do with vLLM's internal batching, but rather the number of tokens sent in each HTTP request from the worker.
+
+| Variable                           | Default | Type/Choices | Description                                                                                               |
+| ---------------------------------- | ------- | ------------ | --------------------------------------------------------------------------------------------------------- |
+| `DEFAULT_BATCH_SIZE`               | `50`    | `int`        | Default and Maximum batch size for token streaming to reduce HTTP calls.                                  |
+| `DEFAULT_MIN_BATCH_SIZE`           | `1`     | `int`        | Batch size for the first request, which will be multiplied by the growth factor every subsequent request. |
+| `DEFAULT_BATCH_SIZE_GROWTH_FACTOR` | `3`     | `float`      | Growth factor for dynamic batch size.                                                                     |
+
+## OpenAI Compatibility Settings
+
+| Variable                            | Default     | Type/Choices     | Description                                                                                                                                                                                                       |
+| ----------------------------------- | ----------- | ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `RAW_OPENAI_OUTPUT`                 | `1`         | boolean as `int` | Enables raw OpenAI SSE format string output when streaming. **Required** to be enabled (which it is by default) for OpenAI compatibility.                                                                         |
+| `OPENAI_SERVED_MODEL_NAME_OVERRIDE` | `None`      | `str`            | Overrides the name of the served model from model repo/path to specified name, which you will then be able to use the value for the `model` parameter when making OpenAI requests                                 |
+| `OPENAI_RESPONSE_ROLE`              | `assistant` | `str`            | Role of the LLM's Response in OpenAI Chat Completions.                                                                                                                                                            |
+| `ENABLE_AUTO_TOOL_CHOICE`           | `false`     | `bool`           | Enables automatic tool selection for supported models. Set to `true` to activate.                                                                                                                                 |
+| `TOOL_CALL_PARSER`                  | `None`      | `str`            | Specifies the parser for tool calls. Options: `mistral`, `hermes`, `llama3_json`, `llama4_json`, `llama4_pythonic`, `granite`, `granite-20b-fc`, `deepseek_v3`, `internlm`, `jamba`, `phi4_mini_json`, `pythonic` |
+| `REASONING_PARSER`                  | `None`      | `str`            | Parser for reasoning-capable models (enables reasoning mode). Examples: `deepseek_r1`, `qwen3`, `granite`, `hunyuan_a13b`. Leave unset to disable.                                                                |
+| `TRUST_REQUEST_CHAT_TEMPLATE`       | `false`     | `bool`           | Allow clients to send custom chat templates in API requests. **Security consideration:** Only enable if you trust your API clients.                                                                               |
+| `RETURN_TOKENS_AS_TOKEN_IDS`        | `false`     | `bool`           | Return token IDs instead of decoded text strings in responses.                                                                                                                                                     |
+| `EXCLUDE_TOOLS_WHEN_TOOL_CHOICE_NONE` | `false`   | `bool`           | Exclude tool definitions from the prompt when `tool_choice` is set to `none`.                                                                                                                                      |
+| `ENABLE_PROMPT_TOKENS_DETAILS`      | `false`     | `bool`           | Include detailed prompt token information in API responses.                                                                                                                                                        |
+| `ENABLE_FORCE_INCLUDE_USAGE`        | `false`     | `bool`           | Always include usage statistics in API responses, even when not requested.                                                                                                                                         |
+| `ENABLE_LOG_OUTPUTS`                | `false`     | `bool`           | Log model outputs for debugging purposes.                                                                                                                                                                          |
+| `LOG_ERROR_STACK`                   | `false`     | `bool`           | Include full stack traces in error responses for debugging.                                                                                                                                                        |
+
+## Serverless & Concurrency Settings
+
+| Variable               | Default | Type/Choices | Description                                                                                                                                                                |
+| ---------------------- | ------- | ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `MAX_CONCURRENCY`      | `30`    | `int`        | Max concurrent requests per worker. vLLM has an internal queue, so you don't have to worry about limiting by VRAM, this is for improving scaling/load balancing efficiency |
+| `DISABLE_LOG_STATS`    | False   | `bool`       | Enables or disables vLLM stats logging.                                                                                                                                    |
+| `ENABLE_LOG_REQUESTS`  | False   | `bool`       | Enables vLLM request logging. (Replaces deprecated `DISABLE_LOG_REQUESTS` in vLLM 0.15.0)                                                                                  |
+
+## Advanced Settings
+
+| Variable                    | Default | Type    | Description                                                                                                                                            |
+| --------------------------- | ------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `MODEL_LOADER_EXTRA_CONFIG` | None    | `dict`  | Extra config for model loader.                                                                                                                         |
+| `PREEMPTION_MODE`           | None    | `str`   | If 'recompute', the engine performs preemption-aware recomputation. If 'save', the engine saves activations into the CPU memory as preemption happens. |
+| `PREEMPTION_CHECK_PERIOD`   | 1.0     | `float` | How frequently the engine checks if a preemption happens.                                                                                              |
+| `PREEMPTION_CPU_CAPACITY`   | 2       | `float` | The percentage of CPU memory used for the saved activations.                                                                                           |
+| `DISABLE_LOGGING_REQUEST`   | False   | `bool`  | Disable logging requests.                                                                                                                              |
+| `MAX_LOG_LEN`               | None    | `int`   | Max number of prompt characters or prompt ID numbers being printed in log.                                                                             |
+
+## UPPERCASED env vars: Pass any engine arg
+
+Any vLLM `AsyncEngineArgs` field can be set via an environment variable using the **UPPERCASED** field name (the same names vLLM uses). The worker auto-discovers all fields from env — no prefix.
+
+**Format:** `<FIELD_NAME_UPPERCASED>=<value>` (e.g. `MAX_MODEL_LEN=4096`)
+
+**Examples:**
+
+| Environment Variable     | vLLM Engine Arg          | Value Example |
+| ------------------------ | ------------------------ | ------------- |
+| `MAX_MODEL_LEN`          | `max_model_len`          | `4096`        |
+| `ENFORCE_EAGER`          | `enforce_eager`          | `true`        |
+| `ENABLE_CHUNKED_PREFILL` | `enable_chunked_prefill` | `true`        |
+| `NUM_SCHEDULER_STEPS`    | `num_scheduler_steps`    | `8`           |
+| `TOKENIZER_POOL_SIZE`    | `tokenizer_pool_size`   | `4`           |
+
+**Backward-compat aliases:** `MODEL_NAME` → `model`, `TOKENIZER_NAME` → `tokenizer`, `MAX_CONTEXT_LEN_TO_CAPTURE` → `max_seq_len_to_capture`, `MODEL_REVISION` → `revision`.
+
+**Notes:**
+- Only valid `AsyncEngineArgs` fields are applied. Unknown keys are silently ignored.
+- Values are automatically cast to the correct type (`int`, `float`, `bool`, `str`, or JSON for `dict`/`list`/`tuple`).
+- For a full list of available engine args, see the [vLLM AsyncEngineArgs documentation](https://docs.vllm.ai/en/latest/configuration/engine_args/).
+
+## Docker Build Arguments
+
+These variables are used when building custom Docker images with models baked in:
+
+| Variable              | Default          | Type  | Description                                       |
+| --------------------- | ---------------- | ----- | ------------------------------------------------- |
+| `BASE_PATH`           | `/runpod-volume` | `str` | Storage directory for huggingface cache and model |
+| `WORKER_CUDA_VERSION` | `12.1.0`         | `str` | CUDA version for the worker image                 |
+
+## Deprecated Variables
+
+⚠️ **The following variables are deprecated and will be removed in future versions:**
+
+| Old Variable                 | New Variable             | Note                                                                 |
+| ---------------------------- | ------------------------ | -------------------------------------------------------------------- |
+| `MAX_CONTEXT_LEN_TO_CAPTURE` | `MAX_SEQ_LEN_TO_CAPTURE` | Use new variable name                                                |
+| `kv_cache_dtype=fp8_e5m2`    | `kv_cache_dtype=fp8`     | Simplified fp8 format                                                |
+| `USE_V2_BLOCK_MANAGER`       | *(removed)*              | V2 block manager is now the default in vLLM 0.13.0, setting ignored  |
+| `VLLM_ATTENTION_BACKEND`     | `ATTENTION_BACKEND`      | Use new env var name (old still works with deprecation warning)      |
+| `DISABLE_LOG_REQUESTS`       | `ENABLE_LOG_REQUESTS`    | Inverted logic in vLLM 0.15.0 (old still works with deprecation warning) |
+
@@ -0,0 +1,343 @@
+# Worker vLLM - Development Conventions & Architecture Guide
+
+## Project Overview
+
+**worker-vllm** is a RunPod serverless worker that provides OpenAI-compatible endpoints for Large Language Model (LLM) inference, powered by the vLLM engine. It enables blazing-fast LLM deployment on RunPod's serverless infrastructure with minimal configuration.
+
+### Core Purpose
+
+- **Primary Function**: Deploy any Hugging Face LLM as an OpenAI-compatible API endpoint
+- **Platform**: RunPod Serverless infrastructure
+- **Engine**: vLLM (high-performance LLM inference engine)
+- **Compatibility**: Drop-in replacement for OpenAI API (Chat Completions, Models)
+
+## High-Level Architecture
+
+### 1. **Entry Point & Request Flow**
+
+```
+RunPod Request → handler.py → JobInput → Engine Selection → vLLM Generation → Streaming Response
+```
+
+**Key Components:**
+
+- `src/handler.py`: Main entry point using RunPod serverless framework
+- `src/utils.py`: Request parsing and utility classes (`JobInput`, `BatchSize`)
+- Two engine modes: OpenAI-compatible vs. standard vLLM
+
+### 2. **Engine Architecture**
+
+#### Core Classes:
+
+- **`vLLMEngine`**: Base engine handling vLLM initialization and generation
+- **`OpenAIvLLMEngine`**: Wrapper providing OpenAI API compatibility
+- **Engine Selection**: Automatic routing based on `job_input.openai_route`
+
+#### Key Design Patterns:
+
+- **Dual API Support**: Same codebase serves both OpenAI-compatible and native vLLM APIs
+- **Streaming by Default**: Token-level streaming with configurable batching
+- **Dynamic Batching**: Adaptive batch sizes that grow from min → max for efficiency
+
+### 3. **Configuration System**
+
+#### Environment-Based Configuration:
+
+- **Single Source of Truth**: All configuration via environment variables
+- **Hierarchical Loading**: `DEFAULT_ARGS` → `os.environ` → `local_model_args.json` (for baked models)
+- **vLLM Argument Mapping**: Automatic translation of env vars to vLLM `AsyncEngineArgs`
+
+#### Key Configuration Files:
+
+- `src/engine_args.py`: Centralized configuration management
+- `src/constants.py`: Default values for core settings
+- `.runpod/hub.json`: Hub UI configuration (CRITICAL: always update when changing defaults)
+- `worker-config.json`: UI form generation for RunPod console (if exists)
+
+## Core Development Concepts
+
+### 1. **Deployment Models**
+
+#### Option 1: Pre-built Images (Recommended)
+
+- **Image**: `runpod/worker-v1-vllm:<version>` (see [GitHub Releases](https://github.com/runpod-workers/worker-vllm/releases))
+- **Configuration**: Entirely via environment variables
+- **Model Loading**: Downloads model at runtime from Hugging Face
+- **Use Case**: Quick deployment, model experimentation
+
+#### Option 2: Baked Model Images
+
+- **Build Process**: Model downloaded during Docker build
+- **Storage**: Model embedded in container image
+- **Configuration**: Stored in `/local_model_args.json`
+- **Use Case**: Production deployments, faster cold starts
+
+### 2. **Request Processing Patterns**
+
+#### Input Handling:
+
+```python
+class JobInput:
+    - llm_input: str | List[Dict] (prompt or messages)
+    - sampling_params: SamplingParams (generation settings)
+    - stream: bool (streaming vs batch response)
+    - openai_route: bool (API compatibility mode)
+    - batch_size configs: Dynamic batching parameters
+```
+
+#### Response Streaming:
+
+- **Batched Streaming**: Tokens grouped into configurable batch sizes
+- **Dynamic Growth**: `min_batch_size * growth_factor^n` up to `max_batch_size`
+- **Usage Tracking**: Input/output token counting for billing
+
+### 3. **Model & Tokenizer Management**
+
+#### Tokenizer Handling:
+
+- **Wrapper Pattern**: `TokenizerWrapper` for consistent chat template application
+- **Special Cases**: Mistral models use vLLM's native tokenizer
+- **Chat Templates**: Automatic application for message-based inputs
+
+#### Model Loading:
+
+- **Multi-GPU Support**: Automatic tensor parallelism detection
+- **Quantization**: Support for AWQ, GPTQ, BitsAndBytes
+- **Caching**: Hugging Face cache management
+
+## Development Patterns & Best Practices
+
+### 1. **Code Organization**
+
+#### File Structure:
+
+```
+src/
+├── handler.py          # RunPod entry point
+├── engine.py          # Core vLLM engines
+├── engine_args.py     # Configuration management
+├── utils.py           # Request parsing & utilities
+├── tokenizer.py       # Tokenizer wrapper
+├── constants.py       # Default constants
+└── download_model.py  # Model downloading logic
+```
+
+#### Separation of Concerns:
+
+- **Engine Logic**: Isolated in `engine.py` classes
+- **Configuration**: Centralized in `engine_args.py`
+- **Request Handling**: Abstracted via `JobInput` class
+- **Platform Integration**: Contained in `handler.py`
+
+### 2. **Error Handling & Logging**
+
+#### Logging Strategy:
+
+- **Structured Logging**: Consistent format across components
+- **Performance Tracking**: Timer decorators for critical operations
+- **Error Context**: Detailed error messages with configuration context
+
+#### Error Responses:
+
+- **OpenAI Compatibility**: Standard OpenAI error format
+- **Graceful Degradation**: Fallback behaviors for edge cases
+
+### 3. **Environment Variable Conventions**
+
+#### Naming Patterns:
+
+- **vLLM Settings**: Match vLLM parameter names (uppercase)
+- **RunPod Settings**: `MAX_CONCURRENCY`, `DEFAULT_BATCH_SIZE`
+- **OpenAI Settings**: `OPENAI_` prefix for compatibility settings
+- **Feature Flags**: `ENABLE_*`, `DISABLE_*` pattern
+
+#### Type Conventions:
+
+- **Booleans**: String 'true'/'false' or int 0/1
+- **Lists**: Comma-separated strings
+- **Objects**: JSON strings for complex configurations
+
+### 4. **Docker & Deployment**
+
+#### Multi-Stage Builds:
+
+- **Base**: CUDA runtime environment
+- **Dependencies**: Python packages and vLLM
+- **Model Download**: Optional model baking stage
+- **Runtime**: Final application layer
+
+#### Build Arguments:
+
+- **MODEL_NAME**: Primary model identifier
+- **BASE_PATH**: Storage location strategy
+- **QUANTIZATION**: Optimization settings
+- **WORKER_CUDA_VERSION**: CUDA compatibility
+
+#### CI/CD Strategy:
+
+- **Development Builds**: All non-main branches → `runpod/worker-v1-vllm:dev-<branch-name>`
+- **Release Builds**: Git tags (numeric) only → `runpod/worker-v1-vllm:<version>`
+- **Dependency Updates**: Automated runpod package version monitoring
+
+#### Docker Bake Configuration:
+
+- **File**: `docker-bake.hcl` (flexible variable-based configuration)
+- **Variables**: `DOCKERHUB_REPO`, `DOCKERHUB_IMG`, `RELEASE_VERSION`, `HUGGINGFACE_ACCESS_TOKEN`
+- **Platform**: `linux/amd64` (GPU-optimized)
+
+## Release & Versioning Strategy
+
+### 1. **Version Tagging**
+
+- **Development**: `dev-<branch-name>` (e.g., `dev-feature-new-api`)
+- **Specific Versions**: `2.7.0`, `2.8.0` (semantic versioning without "v" prefix)
+- **Version Discovery**: Check [GitHub Releases](https://github.com/runpod-workers/worker-vllm/releases) for available versions
+
+### 2. **Release Workflow**
+
+1. **Feature Development**: Work on feature branches → triggers dev builds
+2. **Main Branch Staging**: Merge features to main → stable codebase (no builds)
+3. **Version Release**: Create git tag from main branch (e.g., `2.8.0`) → triggers versioned release + GitHub release
+4. **Docker Hub**: Versioned image pushed with tag
+
+### 3. **Branch Strategy**
+
+- **Feature Branches**: `feature/*`, `fix/*`, `feat/*` etc. → Dev builds
+- **Main Branch**: Stable codebase ready for release (no automatic builds)
+- **Git Tags**: Must be created from main branch for formal version releases
+
+### 4. **Deployment Recommendations**
+
+- **Production**: Use specific version tags (e.g., `2.7.0`) for stability
+- **Development**: Use `dev-<branch>` for testing specific features
+- **Version Selection**: Check [GitHub Releases](https://github.com/runpod-workers/worker-vllm/releases) for available versions
+- **Release Process**: Always tag from main branch: `git checkout main && git tag 2.8.0 && git push origin 2.8.0`
+
+## Performance & Scaling Considerations
+
+### 1. **Memory Management**
+
+- **GPU Utilization**: Default 95% GPU memory utilization
+- **KV Cache**: Configurable cache types (auto, fp8)
+- **Swap Space**: CPU offloading for large contexts
+
+### 2. **Concurrency Patterns**
+
+- **Max Concurrency**: 30 concurrent requests by default
+- **vLLM Queuing**: Internal request batching and scheduling
+- **RunPod Integration**: Concurrency modifier for auto-scaling
+
+### 3. **Optimization Features**
+
+- **Prefix Caching**: Automatic caching of common prefixes
+- **Speculative Decoding**: Draft model acceleration
+- **Chunked Prefill**: Memory-efficient long context handling
+
+## Testing & Development
+
+### 1. **Local Development**
+
+- **Environment**: Virtual environment with GPU support
+- **Configuration**: `.env` files for local testing
+- **Model Testing**: Small models for development (facebook/opt-125m)
+
+### 2. **Docker Development**
+
+- **Build Strategy**: `docker-bake.hcl` for consistent builds
+- **Testing Images**: Separate dev/stable image tags
+- **Layer Caching**: Optimized for rapid iteration
+
+### 3. **Configuration Validation**
+
+- **Argument Matching**: Automatic validation against vLLM parameters
+- **Environment Validation**: Type checking and default value handling
+- **Runtime Validation**: Model compatibility checks
+
+## API Conventions
+
+### 1. **OpenAI Compatibility**
+
+- **Endpoint Mapping**: `/openai/v1/chat/completions`, `/openai/v1/models`
+- **Request Format**: Exact OpenAI request/response schemas
+- **Authentication**: RunPod API key in Authorization header
+- **Model Names**: Hugging Face repo names or custom overrides
+
+### 2. **Native vLLM API**
+
+- **Input Format**: `prompt` or `messages` with `sampling_params`
+- **Streaming**: Token-level streaming with configurable batching
+- **Extensibility**: Support for vLLM-specific features
+
+## Common Patterns & Utilities
+
+### 1. **Configuration Loading**
+
+```python
+# Standard pattern for new configuration options
+def get_engine_args():
+    args = DEFAULT_ARGS
+    args.update(os.environ)  # Environment override
+    args.update(get_local_args())  # Baked model override
+    return match_vllm_args(args)  # Validate against vLLM
+```
+
+### 2. **Error Handling**
+
+```python
+# Standard error response pattern
+def create_error_response(message: str, err_type: str = "BadRequestError"):
+    return ErrorResponse(message=message, type=err_type)
+```
+
+### 3. **Async Generation**
+
+```python
+# Standard streaming pattern
+async def generate(self, job_input: JobInput):
+    async for batch in self._generate_vllm(...):
+        yield batch  # Batch-level yielding for efficiency
+```
+
+## Extension Points
+
+### 1. **New Model Architectures**
+
+- **Engine Args**: Add new parameters in `engine_args.py`
+- **Compatibility**: Update vLLM argument mapping
+- **Validation**: Add architecture-specific validation
+
+### 2. **New API Features**
+
+- **Engine Extension**: Extend `vLLMEngine` or `OpenAIvLLMEngine`
+- **Input Parsing**: Extend `JobInput` class
+- **Response Format**: Add new response generators
+
+### 3. **Performance Optimizations**
+
+- **Batching Strategy**: Modify `BatchSize` class
+- **Memory Management**: Add new caching strategies
+- **Hardware Optimization**: GPU-specific optimizations
+
+## Security & Best Practices
+
+### 1. **Secret Management**
+
+- **Build Secrets**: Docker secrets for HF tokens
+- **Runtime Secrets**: Environment variable injection
+- **Token Handling**: Secure authentication patterns
+
+### 2. **Resource Limits**
+
+- **Memory Bounds**: Configurable GPU memory limits
+- **Request Limits**: Concurrency and timeout controls
+- **Model Safety**: Trust remote code flags
+
+### 3. **Logging Security**
+
+- **Sanitization**: No secrets in logs
+- **Request Logging**: Configurable request/response logging
+- **Performance Monitoring**: Safe metrics collection
+
+---
+
+This guide should be consulted whenever working on the worker-vllm codebase to ensure consistency with established patterns and architectural decisions.
@@ -1,25 +1,4 @@
-DEFAULT_BATCH_SIZE = 30
-DEFAULT_MAX_CONCURRENCY = 300
-
-sampling_param_types = {
-    "n": int,
-    "best_of": int,
-    "presence_penalty": float,
-    "frequency_penalty": float,
-    "repetition_penalty": float,
-    "temperature": float,
-    "top_p": float,
-    "top_k": int,
-    "min_p": float,
-    "use_beam_search": bool,
-    "length_penalty": float,
-    "early_stopping": (bool, str),
-    "stop": (str, list),
-    "stop_token_ids": list,
-    "ignore_eos": bool,
-    "max_tokens": int,
-    "logprobs": int,
-    "prompt_logprobs": int,
-    "skip_special_tokens": bool,
-    "spaces_between_special_tokens": bool,
-}
+DEFAULT_BATCH_SIZE = 50
+DEFAULT_MAX_CONCURRENCY = 30
+DEFAULT_BATCH_SIZE_GROWTH_FACTOR = 3
+DEFAULT_MIN_BATCH_SIZE = 1
@@ -1,22 +1,100 @@
-import argparse
 import os
-from vllm.model_executor.weight_utils import prepare_hf_model_weights
+import json
+import logging
+import glob
+from shutil import rmtree
+from huggingface_hub import snapshot_download
+from utils import timer_decorator
+
+BASE_DIR = "/" 
+TOKENIZER_PATTERNS = [["*.json", "tokenizer*"]]
+MODEL_PATTERNS = [["*.safetensors"], ["*.bin"], ["*.pt"]]
+
+def setup_env():
+    if os.getenv("TESTING_DOWNLOAD") == "1":
+        BASE_DIR = "tmp"
+        os.makedirs(BASE_DIR, exist_ok=True)
+        os.environ.update({
+            "HF_HOME": f"{BASE_DIR}/hf_cache",
+            "MODEL_NAME": "openchat/openchat-3.5-0106",
+            "HF_HUB_ENABLE_HF_TRANSFER": "1",
+            "TENSORIZE": "1",
+            "TENSORIZER_NUM_GPUS": "1",
+            "DTYPE": "auto"
+        })
+
+@timer_decorator
+def download(name, revision, type, cache_dir):
+    if type == "model":
+        pattern_sets = [model_pattern + TOKENIZER_PATTERNS[0] for model_pattern in MODEL_PATTERNS]
+    elif type == "tokenizer":
+        pattern_sets = TOKENIZER_PATTERNS
+    else:
+        raise ValueError(f"Invalid type: {type}")
+    try:
+        for pattern_set in pattern_sets:
+            path = snapshot_download(name, revision=revision, cache_dir=cache_dir, 
+                                    allow_patterns=pattern_set)
+            for pattern in pattern_set:
+                if glob.glob(os.path.join(path, pattern)):
+                    logging.info(f"Successfully downloaded {pattern} model files.")
+                    return path
+    except ValueError:
+        raise ValueError(f"No patterns matching {pattern_sets} found for download.")
+        
+          
+# @timer_decorator
+# def tensorize_model(model_path): TODO: Add back once tensorizer is ready
+#     from vllm.engine.arg_utils import EngineArgs
+#     from vllm.model_executor.model_loader.tensorizer import TensorizerConfig, tensorize_vllm_model
+#     from torch.cuda import device_count
+
+#     tensorizer_num_gpus = int(os.getenv("TENSORIZER_NUM_GPUS", "1"))
+#     if tensorizer_num_gpus > device_count():
+#         raise ValueError(f"TENSORIZER_NUM_GPUS ({tensorizer_num_gpus}) exceeds available GPUs ({device_count()})")
+
+#     dtype = os.getenv("DTYPE", "auto")
+#     serialized_dir = f"{BASE_DIR}/serialized_model"
+#     os.makedirs(serialized_dir, exist_ok=True)
+#     serialized_uri = f"{serialized_dir}/model{'-%03d' if tensorizer_num_gpus > 1 else ''}.tensors"
+    
+#     tensorize_vllm_model(
+#         EngineArgs(model=model_path, tensor_parallel_size=tensorizer_num_gpus, dtype=dtype),
+#         TensorizerConfig(tensorizer_uri=serialized_uri)
+#     )
+#     logging.info("Successfully serialized model to %s", str(serialized_uri))
+#     logging.info("Removing HF Model files after serialization")
+#     rmtree("/".join(model_path.split("/")[:-2]))
+#     return serialized_uri, tensorizer_num_gpus, dtype

 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model", type=str)
-    parser.add_argument(
-        "--download_dir", type=str, default=os.environ.get("MODEL_BASE_PATH")
-    )
-
-    args = parser.parse_args()
-    if not args.model or not args.download_dir:
-        raise ValueError("Must specify model and download_dir")
-
-    if not os.path.exists(args.download_dir):
-        os.makedirs(args.download_dir)
-
-    prepare_hf_model_weights(
-        model_name_or_path=args.model,
-        cache_dir=args.download_dir,
-    )
+    setup_env()
+    cache_dir = os.getenv("HF_HOME")
+    model_name, model_revision = os.getenv("MODEL_NAME"), os.getenv("MODEL_REVISION") or None
+    tokenizer_name, tokenizer_revision = os.getenv("TOKENIZER_NAME") or model_name, os.getenv("TOKENIZER_REVISION") or model_revision
+   
+    model_path = download(model_name, model_revision, "model", cache_dir)   
+  
+    metadata = {
+        "MODEL_NAME": model_path,
+        "MODEL_REVISION": os.getenv("MODEL_REVISION"),
+        "QUANTIZATION": os.getenv("QUANTIZATION"),
+    }   
+    
+    # if os.getenv("TENSORIZE") == "1": TODO: Add back once tensorizer is ready
+    #     serialized_uri, tensorizer_num_gpus, dtype = tensorize_model(model_path)
+    #     metadata.update({
+    #         "MODEL_NAME": serialized_uri,
+    #         "TENSORIZER_URI": serialized_uri,
+    #         "TENSOR_PARALLEL_SIZE": tensorizer_num_gpus,
+    #         "DTYPE": dtype
+    #     })
+        
+    tokenizer_path = download(tokenizer_name, tokenizer_revision, "tokenizer", cache_dir)
+    metadata.update({
+        "TOKENIZER_NAME": tokenizer_path,
+        "TOKENIZER_REVISION": tokenizer_revision
+    })
+    
+    with open(f"{BASE_DIR}/local_model_args.json", "w") as f:
+        json.dump({k: v for k, v in metadata.items() if v not in (None, "")}, f)
@@ -1,89 +1,345 @@
-import os
+import asyncio
+import json
 import logging
-from typing import Union
-import torch
-from vllm import AsyncLLMEngine, AsyncEngineArgs
-from transformers import AutoTokenizer
-from utils import ServerlessConfig
+import os
+import time
+from typing import AsyncGenerator, Optional
+
 from dotenv import load_dotenv
+from vllm import AsyncLLMEngine
+from vllm.entrypoints.logger import RequestLogger
+from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
+from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
+from vllm.entrypoints.openai.completion.protocol import CompletionRequest
+from vllm.entrypoints.openai.completion.serving import OpenAIServingCompletion
+from vllm.entrypoints.openai.engine.protocol import ErrorResponse
+from vllm.entrypoints.openai.models.protocol import BaseModelPath, LoRAModulePath
+from vllm.entrypoints.openai.models.serving import OpenAIServingModels

-
-class Tokenizer:
-    def __init__(self, model_name: str):
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-        self.has_chat_template = bool(self.tokenizer.chat_template)
-
-    def apply_chat_template(self, input: Union[str, list[dict[str, str]]]) -> str:
-        if isinstance(input, list):
-            if not self.has_chat_template:
-                raise ValueError(
-                    "Chat template does not exist for this model, you must provide a single string input instead of a list of messages"
-                )
-        elif isinstance(input, str):
-            input = [{"role": "user", "content": input}]
-        else:
-            raise ValueError("Input must be a string or a list of messages")
-        
-        return self.tokenizer.apply_chat_template(
-            input, tokenize=False, add_generation_prompt=True
-        )
-
+from constants import DEFAULT_BATCH_SIZE, DEFAULT_BATCH_SIZE_GROWTH_FACTOR, DEFAULT_MAX_CONCURRENCY, DEFAULT_MIN_BATCH_SIZE
+from engine_args import get_engine_args
+from tokenizer import TokenizerWrapper
+from utils import BatchSize, DummyRequest, JobInput, create_error_response

 class vLLMEngine:
-    def __init__(self):
+    def __init__(self, engine = None):
        load_dotenv() # For local development
-        self.config = self._initialize_config()
-        self.serverless_config = ServerlessConfig()
-        self.tokenizer = Tokenizer(self.config["model"])
-        self.llm = self._initialize_llm()
+        self.engine_args = get_engine_args()
+        logging.info(f"Engine args: {self.engine_args}")
+        
+        # Initialize vLLM engine first
+        self.llm = self._initialize_llm() if engine is None else engine.llm
+        
+        # Only create custom tokenizer wrapper if not using mistral tokenizer mode
+        # For mistral models, let vLLM handle tokenizer initialization
+        if self.engine_args.tokenizer_mode != 'mistral':
+            self.tokenizer = TokenizerWrapper(self.engine_args.tokenizer or self.engine_args.model, 
+                                              self.engine_args.tokenizer_revision, 
+                                              self.engine_args.trust_remote_code)
+        else:
+            # For mistral models, we'll get the tokenizer from vLLM later
+            self.tokenizer = None
+            
+        self.max_concurrency = int(os.getenv("MAX_CONCURRENCY", DEFAULT_MAX_CONCURRENCY))
+        self.default_batch_size = int(os.getenv("DEFAULT_BATCH_SIZE", DEFAULT_BATCH_SIZE))
+        self.batch_size_growth_factor = int(os.getenv("BATCH_SIZE_GROWTH_FACTOR", DEFAULT_BATCH_SIZE_GROWTH_FACTOR))
+        self.min_batch_size = int(os.getenv("MIN_BATCH_SIZE", DEFAULT_MIN_BATCH_SIZE))

-    def _initialize_config(self):
-        return {
-            "model": os.getenv("MODEL_NAME"),
-            "download_dir": os.getenv("MODEL_BASE_PATH", "/runpod-volume/"),
-            "quantization": self._get_quantization(),
-            "dtype": "auto" if os.getenv("QUANTIZATION") is None else "half",
-            "disable_log_stats": bool(int(os.getenv("DISABLE_LOG_STATS", 1))),
-            "disable_log_requests": bool(int(os.getenv("DISABLE_LOG_REQUESTS", 1))),
-            "trust_remote_code": bool(int(os.getenv("TRUST_REMOTE_CODE", 0))),
-            "gpu_memory_utilization": float(os.getenv("GPU_MEMORY_UTILIZATION", 0.98)),
-            "max_model_len": self._get_max_model_len(),
-            "tensor_parallel_size": self._get_num_gpu_shard(),
+    def _get_tokenizer_for_chat_template(self):
+        """Get tokenizer for chat template application"""
+        if self.tokenizer is not None:
+            return self.tokenizer
+        else:
+            # For mistral models, get tokenizer from vLLM engine
+            # This is a fallback - ideally chat templates should be handled by vLLM directly
+            try:
+                from transformers import AutoTokenizer
+                tokenizer = AutoTokenizer.from_pretrained(
+                    self.engine_args.tokenizer or self.engine_args.model,
+                    revision=self.engine_args.tokenizer_revision or "main",
+                    trust_remote_code=self.engine_args.trust_remote_code
+                )
+                # Create a minimal wrapper
+                class MinimalTokenizerWrapper:
+                    def __init__(self, tokenizer):
+                        self.tokenizer = tokenizer
+                        self.custom_chat_template = os.getenv("CUSTOM_CHAT_TEMPLATE")
+                        self.has_chat_template = bool(self.tokenizer.chat_template) or bool(self.custom_chat_template)
+                        if self.custom_chat_template and isinstance(self.custom_chat_template, str):
+                            self.tokenizer.chat_template = self.custom_chat_template
+                    
+                    def apply_chat_template(self, input):
+                        if isinstance(input, list):
+                            if not self.has_chat_template:
+                                raise ValueError(
+                                    "Chat template does not exist for this model, you must provide a single string input instead of a list of messages"
+                                )
+                        elif isinstance(input, str):
+                            input = [{"role": "user", "content": input}]
+                        else:
+                            raise ValueError("Input must be a string or a list of messages")
+                        
+                        return self.tokenizer.apply_chat_template(
+                            input, tokenize=False, add_generation_prompt=True
+                        )
+                
+                return MinimalTokenizerWrapper(tokenizer)
+            except Exception as e:
+                logging.error(f"Failed to create fallback tokenizer: {e}")
+                raise e
+
+    def dynamic_batch_size(self, current_batch_size, batch_size_growth_factor):
+        return min(current_batch_size*batch_size_growth_factor, self.default_batch_size)
+                           
+    async def generate(self, job_input: JobInput):
+        try:
+            async for batch in self._generate_vllm(
+                llm_input=job_input.llm_input,
+                validated_sampling_params=job_input.sampling_params,
+                batch_size=job_input.max_batch_size,
+                stream=job_input.stream,
+                apply_chat_template=job_input.apply_chat_template,
+                request_id=job_input.request_id,
+                batch_size_growth_factor=job_input.batch_size_growth_factor,
+                min_batch_size=job_input.min_batch_size
+            ):
+                yield batch
+        except Exception as e:
+            yield {"error": create_error_response(str(e)).model_dump()}
+
+    async def _generate_vllm(self, llm_input, validated_sampling_params, batch_size, stream, apply_chat_template, request_id, batch_size_growth_factor, min_batch_size: str) -> AsyncGenerator[dict, None]:
+        if apply_chat_template or isinstance(llm_input, list):
+            tokenizer_wrapper = self._get_tokenizer_for_chat_template()
+            llm_input = tokenizer_wrapper.apply_chat_template(llm_input)
+        results_generator = self.llm.generate(llm_input, validated_sampling_params, request_id)
+        n_responses, n_input_tokens, is_first_output = validated_sampling_params.n, 0, True
+        last_output_texts, token_counters = ["" for _ in range(n_responses)], {"batch": 0, "total": 0}
+
+        batch = {
+            "choices": [{"tokens": []} for _ in range(n_responses)],
        }
+        
+        max_batch_size = batch_size or self.default_batch_size
+        batch_size_growth_factor, min_batch_size = batch_size_growth_factor or self.batch_size_growth_factor, min_batch_size or self.min_batch_size
+        batch_size = BatchSize(max_batch_size, min_batch_size, batch_size_growth_factor)
+    
+
+        async for request_output in results_generator:
+            if is_first_output:  # Count input tokens only once
+                n_input_tokens = len(request_output.prompt_token_ids)
+                is_first_output = False
+
+            for output in request_output.outputs:
+                output_index = output.index
+                token_counters["total"] += 1
+                if stream:
+                    new_output = output.text[len(last_output_texts[output_index]):]
+                    batch["choices"][output_index]["tokens"].append(new_output)
+                    token_counters["batch"] += 1
+
+                    if token_counters["batch"] >= batch_size.current_batch_size:
+                        batch["usage"] = {
+                            "input": n_input_tokens,
+                            "output": token_counters["total"],
+                        }
+                        yield batch
+                        batch = {
+                            "choices": [{"tokens": []} for _ in range(n_responses)],
+                        }
+                        token_counters["batch"] = 0
+                        batch_size.update()
+
+                last_output_texts[output_index] = output.text
+
+        if not stream:
+            for output_index, output in enumerate(last_output_texts):
+                batch["choices"][output_index]["tokens"] = [output]
+            token_counters["batch"] += 1
+
+        if token_counters["batch"] > 0:
+            batch["usage"] = {"input": n_input_tokens, "output": token_counters["total"]}
+            yield batch

    def _initialize_llm(self):
        try:
-            return AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**self.config))
+            start = time.time()
+            engine = AsyncLLMEngine.from_engine_args(self.engine_args)
+            end = time.time()
+            logging.info(f"Initialized vLLM engine in {end - start:.2f}s")
+            return engine
        except Exception as e:
            logging.error("Error initializing vLLM engine: %s", e)
            raise e
+
+
+class OpenAIvLLMEngine(vLLMEngine):
+    def __init__(self, vllm_engine):
+        super().__init__(vllm_engine)
+        self.served_model_name = os.getenv("OPENAI_SERVED_MODEL_NAME_OVERRIDE") or self.engine_args.served_model_name or self.engine_args.model
+        self.response_role = os.getenv("OPENAI_RESPONSE_ROLE") or "assistant"
+        self.lora_adapters = self._load_lora_adapters()
+
+        # Always defer OpenAI engine initialization to the first request.
+        # asyncio.run() creates a temporary event loop that gets closed, but async
+        # components (tokenizer pool, serving engines) bind futures to that loop.
+        # When Runpod's serverless handler runs in its own event loop, those futures
+        # are "attached to a different loop" causing RuntimeError.
+        # This affects all configurations, not just LoRA.
+        self._engines_initialized = False
+        if self.lora_adapters:
+            logging.info(f"LoRA mode: {len(self.lora_adapters)} adapter(s) will load on first request")
+            for adapter in self.lora_adapters:
+                logging.info(f"  - {adapter.name}: {adapter.path}")
+        else:
+            logging.info("OpenAI engines will initialize on first request")
+
+        # Handle both integer and boolean string values for RAW_OPENAI_OUTPUT
+        raw_output_env = os.getenv("RAW_OPENAI_OUTPUT", "1")
+        if raw_output_env.lower() in ('true', 'false'):
+            self.raw_openai_output = raw_output_env.lower() == 'true'
+        else:
+            self.raw_openai_output = bool(int(raw_output_env))
+
+    def _load_lora_adapters(self):
+        adapters = []
+        try:
+            adapters = json.loads(os.getenv("LORA_MODULES", '[]'))
+        except Exception as e:
+            logging.info(f"---Initialized adapter json load error: {e}")
+
+        for i, adapter in enumerate(adapters):
+            try:
+                adapters[i] = LoRAModulePath(**adapter)
+                logging.info(f"---Initialized adapter: {adapter}")
+            except Exception as e:
+                logging.info(f"---Initialized adapter not worked: {e}")
+                continue
+        return adapters
+
+    async def _ensure_engines_initialized(self):
+        """Initialize engines on first request to avoid event loop mismatch.
+
+        In Runpod Serverless, the startup code runs outside the handler's event
+        loop. Deferring initialization to the first request ensures all async
+        components (tokenizer pool, serving engines, LoRA state) are created in
+        the correct event loop context.
+        """
+        if not self._engines_initialized:
+            logging.info("Initializing OpenAI serving engines...")
+            await self._initialize_engines()
+            self._engines_initialized = True
+            logging.info("OpenAI serving engines initialized successfully")
+
+    async def _initialize_engines(self):
+        self.model_config = self.llm.model_config
+        self.base_model_paths = [
+            BaseModelPath(name=self.served_model_name, model_path=self.engine_args.model)
+        ]
+
+        self.serving_models = OpenAIServingModels(
+            engine_client=self.llm,
+            base_model_paths=self.base_model_paths,
+            lora_modules=self.lora_adapters,
+        )
+        await self.serving_models.init_static_loras()
        
-    def _get_num_gpu_shard(self):
-        final_num_gpu_shard = 1
-        if bool(int(os.getenv("USE_TENSOR_PARALLEL", 0))):
-            env_num_gpu_shard = int(os.getenv("TENSOR_PARALLEL_SIZE", 1))
-            num_gpu_available = torch.cuda.device_count()
-            final_num_gpu_shard = min(env_num_gpu_shard, num_gpu_available)
-            logging.info("Using %s GPU shards", final_num_gpu_shard)
-        return final_num_gpu_shard
-    
-    def _get_max_model_len(self):
-        max_model_len = os.getenv("MAX_MODEL_LEN")
-        return int(max_model_len) if max_model_len is not None else None
-    
-    def _get_n_current_jobs(self):
-        total_sequences = len(self.llm.engine.scheduler.waiting) + len(self.llm.engine.scheduler.swapped) + len(self.llm.engine.scheduler.running)
-        return total_sequences
+        # Get chat template from vLLM tokenizer if available
+        chat_template = None
+        if self.tokenizer and hasattr(self.tokenizer, 'tokenizer'):
+            chat_template = self.tokenizer.tokenizer.chat_template
+        
+        self.chat_engine = OpenAIServingChat(
+            engine_client=self.llm, 
+            models=self.serving_models,
+            response_role=self.response_role,
+            request_logger=None,
+            chat_template=chat_template,
+            chat_template_content_format="auto",
+            trust_request_chat_template=os.getenv('TRUST_REQUEST_CHAT_TEMPLATE', 'false').lower() == 'true',
+            return_tokens_as_token_ids=os.getenv('RETURN_TOKENS_AS_TOKEN_IDS', 'false').lower() == 'true',
+            reasoning_parser=os.getenv('REASONING_PARSER', "") or "",
+            enable_auto_tools=os.getenv('ENABLE_AUTO_TOOL_CHOICE', 'false').lower() == 'true',
+            exclude_tools_when_tool_choice_none=os.getenv('EXCLUDE_TOOLS_WHEN_TOOL_CHOICE_NONE', 'false').lower() == 'true',
+            tool_parser=os.getenv('TOOL_CALL_PARSER', "") or None,
+            enable_prompt_tokens_details=os.getenv('ENABLE_PROMPT_TOKENS_DETAILS', 'false').lower() == 'true',
+            enable_force_include_usage=os.getenv('ENABLE_FORCE_INCLUDE_USAGE', 'false').lower() == 'true',
+            enable_log_outputs=os.getenv('ENABLE_LOG_OUTPUTS', 'false').lower() == 'true',
+            log_error_stack=os.getenv('LOG_ERROR_STACK', 'false').lower() == 'true',
+        )
+        self.completion_engine = OpenAIServingCompletion(
+            engine_client=self.llm,
+            models=self.serving_models,
+            request_logger=None,
+            return_tokens_as_token_ids=os.getenv('RETURN_TOKENS_AS_TOKEN_IDS', 'false').lower() == 'true',
+            enable_prompt_tokens_details=os.getenv('ENABLE_PROMPT_TOKENS_DETAILS', 'false').lower() == 'true',
+            enable_force_include_usage=os.getenv('ENABLE_FORCE_INCLUDE_USAGE', 'false').lower() == 'true',
+            log_error_stack=os.getenv('LOG_ERROR_STACK', 'false').lower() == 'true',
+        )

-    def _get_quantization(self):
-        quantization = os.getenv("QUANTIZATION", "").lower()
-        return quantization if quantization in ["awq", "squeezellm", "gptq"] else None
-    
-    def concurrency_modifier(self, current_concurrency):
-        n_current_jobs = self._get_n_current_jobs()
-        requested_concurrency = max(0, self.serverless_config.max_concurrency - n_current_jobs)
-        if not self.config["disable_log_stats"]:
-            logging.info("Current Jobs: %s", n_current_jobs)
-            logging.info("Concurrency Modifier Requested Jobs: %s", requested_concurrency)
-        return requested_concurrency
+        if hasattr(self.chat_engine, 'warmup'):
+            await self.chat_engine.warmup()

+    async def generate(self, openai_request: JobInput):
+        # Ensure engines are ready (no-op if already initialized at startup)
+        await self._ensure_engines_initialized()
+
+        if openai_request.openai_route == "/v1/models":
+            yield await self._handle_model_request()
+        elif openai_request.openai_route in ["/v1/chat/completions", "/v1/completions"]:
+            async for response in self._handle_chat_or_completion_request(openai_request):
+                yield response
+        else:
+            yield create_error_response("Invalid route").model_dump()
+    
+    async def _handle_model_request(self):
+        models = await self.serving_models.show_available_models()
+        return models.model_dump()
+    
+    async def _handle_chat_or_completion_request(self, openai_request: JobInput):
+        if openai_request.openai_route == "/v1/chat/completions":
+            request_class = ChatCompletionRequest
+            generator_function = self.chat_engine.create_chat_completion
+        elif openai_request.openai_route == "/v1/completions":
+            request_class = CompletionRequest
+            generator_function = self.completion_engine.create_completion
+        
+        try:
+            request = request_class(
+                **openai_request.openai_input
+            )
+        except Exception as e:
+            yield create_error_response(str(e)).model_dump()
+            return
+        
+        dummy_request = DummyRequest()
+        response_generator = await generator_function(request, raw_request=dummy_request)
+
+        if not openai_request.openai_input.get("stream") or isinstance(response_generator, ErrorResponse):
+            yield response_generator.model_dump()
+        else:
+            batch = []
+            batch_token_counter = 0
+            batch_size = BatchSize(self.default_batch_size, self.min_batch_size, self.batch_size_growth_factor)
+        
+            async for chunk_str in response_generator:
+                if "data" in chunk_str:
+                    if self.raw_openai_output:
+                        data = chunk_str
+                    elif "[DONE]" in chunk_str:
+                        continue
+                    else:
+                        data = json.loads(chunk_str.removeprefix("data: ").rstrip("\n\n")) if not self.raw_openai_output else chunk_str
+                    batch.append(data)
+                    batch_token_counter += 1
+                    if batch_token_counter >= batch_size.current_batch_size:
+                        if self.raw_openai_output:
+                            batch = "".join(batch)
+                        yield batch
+                        batch = []
+                        batch_token_counter = 0
+                        batch_size.update()
+            if batch:
+                if self.raw_openai_output:
+                    batch = "".join(batch)
+                yield batch
+            
@@ -0,0 +1,461 @@
+import os
+import json
+import logging
+from typing import get_origin, get_args
+from torch.cuda import device_count
+from vllm import AsyncEngineArgs
+from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
+from src.utils import convert_limit_mm_per_prompt
+
+# Backward-compat: env var names users already know → engine arg name
+ENV_ALIASES = {
+    "MODEL_NAME": "model",
+    "MODEL_REVISION": "revision",
+    "TOKENIZER_NAME": "tokenizer",
+}
+
+# Literal defaults from original worker (used when env/local do not set a value)
+DEFAULT_ARGS = {
+    "disable_log_stats": False,
+    "enable_log_requests": False,
+    "gpu_memory_utilization": 0.95,
+    "pipeline_parallel_size": 1,
+    "tensor_parallel_size": 1,
+    "skip_tokenizer_init": False,
+    "tokenizer_mode": "auto",
+    "trust_remote_code": False,
+    "load_format": "auto",
+    "dtype": "auto",
+    "kv_cache_dtype": "auto",
+    "seed": 0,
+    "worker_use_ray": False,
+    "block_size": 16,
+    "enable_prefix_caching": False,
+    "disable_sliding_window": False,
+    "swap_space": 4,
+    "cpu_offload_gb": 0,
+    "max_num_seqs": 256,
+    "max_logprobs": 20,
+    "enforce_eager": False,
+    "max_seq_len_to_capture": 8192,
+    "disable_custom_all_reduce": False,
+    "tokenizer_pool_size": 0,
+    "tokenizer_pool_type": "ray",
+    "enable_lora": False,
+    "max_loras": 1,
+    "max_lora_rank": 16,
+    "enable_prompt_adapter": False,
+    "max_prompt_adapters": 1,
+    "max_prompt_adapter_token": 0,
+    "fully_sharded_loras": False,
+    "lora_extra_vocab_size": 256,
+    "lora_dtype": "auto",
+    "device": "auto",
+    "ray_workers_use_nsight": False,
+    "num_lookahead_slots": 0,
+    "scheduler_delay_factor": 0.0,
+    "guided_decoding_backend": "outlines",
+    "spec_decoding_acceptance_method": "rejection_sampler",
+    "stream_interval": 1,
+
+}
+
+
+def _resolve_field_type(field_type: type) -> type:
+    """Resolve Optional/Union to the concrete type for conversion."""
+    origin = get_origin(field_type)
+    args = get_args(field_type) if hasattr(field_type, "__args__") else ()
+    if origin is not None:
+        # Optional[X] is Union[X, None]; X | None is UnionType
+        non_none = [a for a in args if a is not type(None)]
+        if non_none:
+            return non_none[0]
+    return field_type
+
+
+def _convert_env_value_to_field_type(value: str, field_name: str, field_type: type):
+    """Convert env var string to the type expected by AsyncEngineArgs for this field."""
+    val = value.strip() if isinstance(value, str) else value
+    if val in ("", "None", "none"):
+        args = get_args(field_type) if hasattr(field_type, "__args__") else ()
+        if type(None) in (args or ()):
+            return None
+        raise ValueError("empty value not allowed for non-optional field")
+    effective_type = _resolve_field_type(field_type)
+    # bool
+    if effective_type is bool:
+        return str(val).lower() in ("true", "1", "yes", "on")
+    # int
+    if effective_type is int:
+        return int(val)
+    # float
+    if effective_type is float:
+        return float(val)
+    # str
+    if effective_type is str:
+        return str(val)
+    # dict, list, or complex (try JSON)
+    origin = get_origin(effective_type)
+    if effective_type in (dict, list) or origin in (dict, list):
+        try:
+            return json.loads(val)
+        except json.JSONDecodeError:
+            return val
+    # tuple (e.g. long_lora_scaling_factors) — comma-separated or JSON array
+    if effective_type is tuple or origin is tuple:
+        args = get_args(field_type) if hasattr(field_type, "__args__") else ()
+        elem_types = [a for a in args if a is not Ellipsis]
+        elem_type = elem_types[0] if elem_types else str
+        try:
+            parsed = json.loads(val)
+            if isinstance(parsed, list):
+                return tuple(elem_type(x) for x in parsed)
+        except (json.JSONDecodeError, TypeError):
+            pass
+        return tuple(elem_type(x.strip()) for x in str(val).split(",") if x.strip())
+    # Fallback: try int, float, then str
+    try:
+        return int(val)
+    except ValueError:
+        pass
+    try:
+        return float(val)
+    except ValueError:
+        pass
+    return str(val)
+
+
+def _get_args_from_env_auto_discover() -> dict:
+    """Auto-discover engine args from env vars using UPPERCASED field names.
+
+    For every field in AsyncEngineArgs, check os.getenv(FIELD_NAME).
+    E.g. MAX_MODEL_LEN=4096 -> max_model_len=4096.
+    Uses same type conversion as before; supports all vLLM engine args without manual listing.
+    """
+    args = {}
+    valid_fields = AsyncEngineArgs.__dataclass_fields__
+    for field_name, field in valid_fields.items():
+        env_key = field_name.upper()
+        value = os.environ.get(env_key)
+        if value is None:
+            continue
+        try:
+            args[field_name] = _convert_env_value_to_field_type(
+                value, field_name, field.type
+            )
+        except (ValueError, TypeError, json.JSONDecodeError) as e:
+            logging.warning(
+                "Skip env %s=%r: %s", env_key, value, e
+            )
+    return args
+
+
+def _apply_env_aliases(args: dict) -> None:
+    """Apply ENV_ALIASES: if MODEL_NAME etc. are set, set the target engine arg."""
+    valid_fields = AsyncEngineArgs.__dataclass_fields__
+    for alias, target in ENV_ALIASES.items():
+        value = os.environ.get(alias)
+        if value is None or target not in valid_fields:
+            continue
+        try:
+            args[target] = _convert_env_value_to_field_type(
+                value, target, valid_fields[target].type
+            )
+        except (ValueError, TypeError, json.JSONDecodeError) as e:
+            logging.warning("Skip env alias %s=%r: %s", alias, value, e)
+
+def get_speculative_config():
+    """Build speculative decoding configuration from environment variables.
+
+    Supports two modes:
+    1. Full JSON config via SPECULATIVE_CONFIG env var
+    2. Individual env vars for common settings
+    """
+    # Option 1: Full JSON configuration
+    spec_config_json = os.getenv('SPECULATIVE_CONFIG')
+    if spec_config_json:
+        try:
+            config = json.loads(spec_config_json)
+            logging.info(f"Using speculative config from SPECULATIVE_CONFIG: {config}")
+            return config
+        except json.JSONDecodeError as e:
+            logging.error(f"Failed to parse SPECULATIVE_CONFIG JSON: {e}")
+            return None
+
+    # Option 2: Build config from individual environment variables
+    spec_method = os.getenv('SPECULATIVE_METHOD')
+    spec_model = os.getenv('SPECULATIVE_MODEL')
+    _num_spec_tokens = os.getenv('NUM_SPECULATIVE_TOKENS')
+    _ngram_max = os.getenv('NGRAM_PROMPT_LOOKUP_MAX')
+    _ngram_min = os.getenv('NGRAM_PROMPT_LOOKUP_MIN')
+
+    # Convert numeric vars to int so '0' (hub.json default) is treated as unset
+    num_spec_tokens = (int(_num_spec_tokens) or None) if _num_spec_tokens else None
+    ngram_max = (int(_ngram_max) or None) if _ngram_max else None
+    ngram_min = (int(_ngram_min) or None) if _ngram_min else None
+
+    if not any([spec_method, spec_model, ngram_max]):
+        return None
+
+    config = {}
+
+    # Determine method
+    if spec_method:
+        config['method'] = spec_method
+    elif ngram_max and not spec_model:
+        config['method'] = 'ngram'
+    elif spec_model:
+        model_lower = spec_model.lower()
+        if 'eagle3' in model_lower:
+            config['method'] = 'eagle3'
+        elif 'eagle' in model_lower:
+            config['method'] = 'eagle'
+        elif 'medusa' in model_lower:
+            config['method'] = 'medusa'
+        else:
+            config['method'] = 'draft_model'
+
+    if spec_model:
+        config['model'] = spec_model
+    if num_spec_tokens:
+        config['num_speculative_tokens'] = num_spec_tokens
+    if ngram_max:
+        config['prompt_lookup_max'] = ngram_max
+    if ngram_min:
+        config['prompt_lookup_min'] = ngram_min
+
+    draft_tp = os.getenv('SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE')
+    if draft_tp:
+        config['draft_tensor_parallel_size'] = int(draft_tp)
+
+    spec_max_len = os.getenv('SPECULATIVE_MAX_MODEL_LEN')
+    if spec_max_len:
+        config['max_model_len'] = int(spec_max_len)
+
+    disable_batch = os.getenv('SPECULATIVE_DISABLE_BY_BATCH_SIZE')
+    if disable_batch:
+        config['disable_by_batch_size'] = int(disable_batch)
+
+    spec_quant = os.getenv('SPECULATIVE_QUANTIZATION')
+    if spec_quant:
+        config['quantization'] = spec_quant
+
+    spec_revision = os.getenv('SPECULATIVE_MODEL_REVISION')
+    if spec_revision:
+        config['revision'] = spec_revision
+
+    spec_eager = os.getenv('SPECULATIVE_ENFORCE_EAGER')
+    if spec_eager:
+        config['enforce_eager'] = spec_eager.lower() == 'true'
+
+    if config:
+        logging.info(f"Built speculative config from env vars: {config}")
+        return config
+
+    return None
+
+
+def _resolve_max_model_len(model, trust_remote_code=False, revision=None):
+    """Resolve max_model_len from the model's HuggingFace config."""
+    try:
+        from transformers import AutoConfig
+        config = AutoConfig.from_pretrained(
+            model,
+            trust_remote_code=trust_remote_code,
+            revision=revision,
+        )
+        for attr in ('max_position_embeddings', 'n_positions', 'max_seq_len', 'seq_length'):
+            val = getattr(config, attr, None)
+            if val is not None:
+                logging.info(f"Resolved max_model_len={val} from model config ({attr})")
+                return val
+    except Exception as e:
+        logging.warning(f"Could not resolve max_model_len from model config: {e}")
+    return None
+
+
+def _local_args_to_engine_args(local: dict) -> dict:
+    """Map local args (e.g. from /local_model_args.json) to engine arg names and filter."""
+    valid = AsyncEngineArgs.__dataclass_fields__
+    out = {}
+    for k, v in local.items():
+        target = ENV_ALIASES.get(k, k.lower().replace("-", "_"))
+        if target not in valid or v in (None, "", "None"):
+            continue
+        out[target] = v
+    return out
+
+
+def _sanitize_hf_overrides(hf_overrides: dict) -> dict | None:
+    """Strip rope_scaling from hf_overrides sub-configs if vLLM rejects them.
+
+    Older vLLM (<0.7) required explicit mrope rope_scaling in hf_overrides for
+    models like Qwen2-VL. Newer vLLM auto-detects mrope and raises a ValueError
+    in patch_rope_scaling_dict when it finds conflicting rope_type values. Strip
+    the offending rope_scaling so the model loads with its native config.
+    """
+    if not isinstance(hf_overrides, dict):
+        return hf_overrides
+
+    try:
+        from vllm.transformers_utils.config import patch_rope_scaling_dict
+    except ImportError:
+        return hf_overrides
+
+    import copy
+    cleaned = {}
+    changed = False
+    for key, value in hf_overrides.items():
+        if isinstance(value, dict) and "rope_scaling" in value:
+            rope_scaling = value.get("rope_scaling")
+            if isinstance(rope_scaling, dict):
+                try:
+                    patch_rope_scaling_dict(copy.deepcopy(rope_scaling))
+                except (ValueError, Exception) as e:
+                    logging.warning(
+                        "Stripping hf_overrides['%s']['rope_scaling'] because vLLM "
+                        "rejected it (%s). Newer vLLM auto-detects rope scaling from "
+                        "the model config.", key, e
+                    )
+                    stripped = {k: v for k, v in value.items() if k != "rope_scaling"}
+                    cleaned[key] = stripped if stripped else None
+                    changed = True
+                    continue
+        cleaned[key] = value
+
+    if not changed:
+        return hf_overrides
+
+    result = {k: v for k, v in cleaned.items() if v is not None}
+    return result or None
+
+
+def get_local_args():
+    """
+    Retrieve local arguments from a JSON file.
+
+    Returns:
+        dict: Local arguments.
+    """
+    if not os.path.exists("/local_model_args.json"):
+        return {}
+
+    with open("/local_model_args.json", "r") as f:
+        local_args = json.load(f)
+
+    if local_args.get("MODEL_NAME") is None:
+        logging.warning("Model name not found in /local_model_args.json. There maybe was a problem when baking the model in.")
+
+    logging.info(f"Using baked in model with args: {local_args}")
+    os.environ["TRANSFORMERS_OFFLINE"] = "1"
+    os.environ["HF_HUB_OFFLINE"] = "1"
+
+    return local_args
+def get_engine_args():
+    # Start with worker custom defaults (only where we differ from vLLM)
+    args = dict(DEFAULT_ARGS)
+
+    # Auto-discover: every AsyncEngineArgs field from env UPPERCASED (e.g. MAX_MODEL_LEN)
+    args.update(_get_args_from_env_auto_discover())
+
+    # Backward-compat aliases (MODEL_NAME → model, etc.)
+    _apply_env_aliases(args)
+
+    # Local baked-in model overrides
+    local = get_local_args()
+    if local:
+        args.update(_local_args_to_engine_args(local))
+
+    # Filter to valid engine args and drop sentinel empty values
+    valid_fields = AsyncEngineArgs.__dataclass_fields__
+    args = {
+        k: v for k, v in args.items()
+        if k in valid_fields and v not in (None, "", "None")
+    }
+
+    # Special conversion for limit_mm_per_prompt (e.g. "image=1,video=0")
+    limit_mm_env = os.getenv("LIMIT_MM_PER_PROMPT")
+    if limit_mm_env is not None:
+        args["limit_mm_per_prompt"] = convert_limit_mm_per_prompt(limit_mm_env)
+
+    # if args.get("TENSORIZER_URI"): TODO: add back once tensorizer is ready
+    #     args["load_format"] = "tensorizer"
+    #     args["model_loader_extra_config"] = TensorizerConfig(tensorizer_uri=args["TENSORIZER_URI"], num_readers=None)
+    #     logging.info(f"Using tensorized model from {args['TENSORIZER_URI']}")
+
+    if "hf_overrides" in args:
+        sanitized = _sanitize_hf_overrides(args["hf_overrides"])
+        if sanitized:
+            args["hf_overrides"] = sanitized
+        else:
+            del args["hf_overrides"]
+
+    if args.get("load_format") == "bitsandbytes":
+        args["quantization"] = args["load_format"]
+    
+    # Set tensor parallel size and max parallel loading workers if more than 1 GPU is available
+    num_gpus = device_count()
+    if num_gpus > 1:
+        args["tensor_parallel_size"] = num_gpus
+        args["max_parallel_loading_workers"] = None
+        if os.getenv("MAX_PARALLEL_LOADING_WORKERS"):
+            logging.warning("Overriding MAX_PARALLEL_LOADING_WORKERS with None because more than 1 GPU is available.")
+    
+    # Deprecated env args backwards compatibility
+    if args.get("kv_cache_dtype") == "fp8_e5m2":
+        args["kv_cache_dtype"] = "fp8"
+        logging.warning("Using fp8_e5m2 is deprecated. Please use fp8 instead.")
+    if os.getenv("MAX_CONTEXT_LEN_TO_CAPTURE"):
+        args["max_seq_len_to_capture"] = int(os.getenv("MAX_CONTEXT_LEN_TO_CAPTURE"))
+        logging.warning("Using MAX_CONTEXT_LEN_TO_CAPTURE is deprecated. Please use MAX_SEQ_LEN_TO_CAPTURE instead.")
+        
+    # if "gemma-2" in args.get("model", "").lower():
+    #     os.environ["VLLM_ATTENTION_BACKEND"] = "FLASHINFER"
+    #     logging.info("Using FLASHINFER for gemma-2 model.")
+    
+    # Set max_num_batched_tokens to max_model_len for unlimited batching.
+    # vLLM defaults max_num_batched_tokens to 2048 when None, which is too low.
+
+    if args.get("max_model_len") == 0:
+        args["max_model_len"] = None
+
+    if args.get("max_num_batched_tokens") == 0:
+        args["max_num_batched_tokens"] = None
+
+    if args.get("max_num_batched_tokens") is None:
+        max_model_len = args.get("max_model_len")
+        if max_model_len is None:
+            max_model_len = _resolve_max_model_len(
+                args.get("model"),
+                trust_remote_code=args.get("trust_remote_code", False),
+                revision=args.get("revision"),
+            )
+        if max_model_len is not None:
+            args["max_num_batched_tokens"] = max_model_len
+            logging.info(f"Setting max_num_batched_tokens to {max_model_len}")
+    
+    # VLLM_ATTENTION_BACKEND is deprecated, migrate to attention_backend
+    if os.getenv('VLLM_ATTENTION_BACKEND'):
+        logging.warning(
+            "VLLM_ATTENTION_BACKEND env var is deprecated. "
+            "Use ATTENTION_BACKEND instead (maps to --attention-backend CLI arg)."
+        )
+        if not args.get('attention_backend'):
+            args['attention_backend'] = os.getenv('VLLM_ATTENTION_BACKEND')
+
+    # DISABLE_LOG_REQUESTS is deprecated, use ENABLE_LOG_REQUESTS instead
+    if os.getenv('DISABLE_LOG_REQUESTS'):
+        logging.warning(
+            "DISABLE_LOG_REQUESTS env var is deprecated. "
+            "Use ENABLE_LOG_REQUESTS instead (default: False)."
+        )
+        # Honor old behavior: if DISABLE_LOG_REQUESTS=true, don't enable logging
+        if os.getenv('DISABLE_LOG_REQUESTS', 'False').lower() == 'true':
+            args['enable_log_requests'] = False
+
+    # Add speculative decoding configuration if present
+    speculative_config = get_speculative_config()
+    if speculative_config:
+        args["speculative_config"] = speculative_config
+
+    return AsyncEngineArgs(**args)
@@ -1,67 +1,55 @@
-#!/usr/bin/env python
-from typing import Generator
+import sys
+import multiprocessing
+import traceback
 import runpod
-from utils import validate_sampling_params, random_uuid
-from engine import vLLMEngine
+from runpod import RunPodLogger

-vllm_engine = vLLMEngine()
-async def handler(job: dict) -> Generator[dict, None, None]:
-    job_input = job["input"]
-    llm_input = job_input.get("messages", job_input.get("prompt"))
-    apply_chat_template = job_input.get("apply_chat_template", False)
+log = RunPodLogger()

-    if apply_chat_template or isinstance(llm_input, list):
-        llm_input = vllm_engine.tokenizer.apply_chat_template(llm_input)
+vllm_engine = None
+openai_engine = None

-    stream = job_input.get("stream", False)
-    batch_size = job_input.get("batch_size", vllm_engine.serverless_config.default_batch_size)
-    sampling_params = job_input.get("sampling_params", {})

-    validated_params = validate_sampling_params(sampling_params)
-    request_id = random_uuid()
-    results_generator = vllm_engine.llm.generate(
-        llm_input, validated_params, request_id
+async def handler(job):
+    try:
+        from utils import JobInput
+        job_input = JobInput(job["input"])
+        engine = openai_engine if job_input.openai_route else vllm_engine
+        results_generator = engine.generate(job_input)
+        async for batch in results_generator:
+            yield batch
+    except Exception as e:
+        error_str = str(e)
+        full_traceback = traceback.format_exc()
+
+        log.error(f"Error during inference: {error_str}")
+        log.error(f"Full traceback:\n{full_traceback}")
+
+        # CUDA errors = worker is broken, exit to let RunPod spin up a healthy one
+        if "CUDA" in error_str or "cuda" in error_str:
+            log.error("Terminating worker due to CUDA/GPU error")
+            sys.exit(1)
+
+        yield {"error": error_str}
+
+
+# Only run in main process to prevent re-initialization when vLLM spawns worker subprocesses
+if __name__ == "__main__" or multiprocessing.current_process().name == "MainProcess":
+
+    try:
+        from engine import vLLMEngine, OpenAIvLLMEngine
+
+        vllm_engine = vLLMEngine()
+        openai_engine = OpenAIvLLMEngine(vllm_engine)
+        log.info("vLLM engines initialized successfully")
+    except Exception as e:
+        log.error(f"Worker startup failed: {e}\n{traceback.format_exc()}")
+        sys.exit(1)
+
+    runpod.serverless.start(
+        {
+            "handler": handler,
+            "concurrency_modifier": lambda x: vllm_engine.max_concurrency if vllm_engine else 1,
+            "return_aggregate_stream": True,
+        }
    )
-
-    batch = {"tokens": []}
-    last_output_text = ""
-    n_input_tokens, is_first_output = 0, True
-    
-    async for request_output in results_generator:
-        if is_first_output: # Count input tokens only once
-            n_input_tokens = len(request_output.prompt_token_ids)
-            is_first_output = False
-            
-        for output in request_output.outputs:
-            if stream:
-                
-                batch["tokens"].append(
-                    output.text[len(last_output_text):]
-                )
-                finished = request_output.finished
-                if len(batch["tokens"]) >= batch_size or finished:
-                    batch["usage"] = {
-                        "input": n_input_tokens,
-                        "output": len(output.token_ids),
-                    }
-                    batch["finished"] = finished
-                    yield batch
-                    batch = {"tokens": []}
-                    
-            last_output_text = output.text
-    
-    if not stream:
-        yield {"tokens": [last_output_text], 
-                "usage": {
-                        "input": n_input_tokens,
-                        "output": len(output.token_ids),
-                    },
-                "finished": True}
-
-runpod.serverless.start(
-    {
-        "handler": handler,
-        "concurrency_modifier": lambda x: vllm_engine.serverless_config.max_concurrency,
-        "return_aggregate_stream": True,
-    }
-)
@@ -0,0 +1,27 @@
+from transformers import AutoTokenizer
+import os
+from typing import Union
+
+class TokenizerWrapper:
+    def __init__(self, tokenizer_name_or_path, tokenizer_revision, trust_remote_code):
+        print(f"tokenizer_name_or_path: {tokenizer_name_or_path}, tokenizer_revision: {tokenizer_revision}, trust_remote_code: {trust_remote_code}")
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, revision=tokenizer_revision or "main", trust_remote_code=trust_remote_code)
+        self.custom_chat_template = os.getenv("CUSTOM_CHAT_TEMPLATE")
+        self.has_chat_template = bool(self.tokenizer.chat_template) or bool(self.custom_chat_template)
+        if self.custom_chat_template and isinstance(self.custom_chat_template, str):
+            self.tokenizer.chat_template = self.custom_chat_template
+
+    def apply_chat_template(self, input: Union[str, list[dict[str, str]]]) -> str:
+        if isinstance(input, list):
+            if not self.has_chat_template:
+                raise ValueError(
+                    "Chat template does not exist for this model, you must provide a single string input instead of a list of messages"
+                )
+        elif isinstance(input, str):
+            input = [{"role": "user", "content": input}]
+        else:
+            raise ValueError("Input must be a string or a list of messages")
+        
+        return self.tokenizer.apply_chat_template(
+            input, tokenize=False, add_generation_prompt=True
+        )
@@ -1,51 +1,105 @@
 import os
 import logging
-from typing import Any, Dict
-from vllm import SamplingParams
-from vllm.utils import random_uuid
-from constants import sampling_param_types, DEFAULT_BATCH_SIZE, DEFAULT_MAX_CONCURRENCY
+from http import HTTPStatus
+from functools import wraps
+from time import time
+
+try:
+    from vllm.utils import random_uuid
+    from vllm.entrypoints.openai.engine.protocol import ErrorResponse, ErrorInfo, RequestResponseMetadata
+    from vllm import SamplingParams
+except ImportError:
+    logging.warning("Error importing vllm, skipping related imports. This is ONLY expected when baking model into docker image from a machine without GPUs")
+    pass

 logging.basicConfig(level=logging.INFO)

+# Updated to parse multiple comma-separated multimodal limits (e.g., 'image=1,video=0')
+def convert_limit_mm_per_prompt(input_string: str):
+    result = {}
+    pairs = input_string.split(',')
+    for pair in pairs:
+        key, value = pair.split('=')
+        result[key] = int(value)
+    return result

-class ServerlessConfig:
+def count_physical_cores():
+    with open('/proc/cpuinfo') as f:
+        content = f.readlines()
+
+    cores = set()
+    current_physical_id = None
+    current_core_id = None
+
+    for line in content:
+        if 'physical id' in line:
+            current_physical_id = line.strip().split(': ')[1]
+        elif 'core id' in line:
+            current_core_id = line.strip().split(': ')[1]
+            cores.add((current_physical_id, current_core_id))
+
+    return len(cores)
+
+
+class JobInput:
+    def __init__(self, job):
+        self.llm_input = job.get("messages", job.get("prompt"))
+        self.stream = job.get("stream", False)
+        self.max_batch_size = job.get("max_batch_size")
+        self.apply_chat_template = job.get("apply_chat_template", False)
+        self.use_openai_format = job.get("use_openai_format", False)
+        samp_param = job.get("sampling_params", {})
+        if "max_tokens" not in samp_param:
+            samp_param["max_tokens"] = 100
+        self.sampling_params = SamplingParams(**samp_param)
+        # self.sampling_params = SamplingParams(max_tokens=100, **job.get("sampling_params", {}))
+        self.request_id = random_uuid()
+        batch_size_growth_factor = job.get("batch_size_growth_factor")
+        self.batch_size_growth_factor = float(batch_size_growth_factor) if batch_size_growth_factor else None 
+        min_batch_size = job.get("min_batch_size")
+        self.min_batch_size = int(min_batch_size) if min_batch_size else None 
+        self.openai_route = job.get("openai_route")
+        self.openai_input = job.get("openai_input")
+class DummyState:
    def __init__(self):
-        self._max_concurrency = int(
-            os.environ.get("MAX_CONCURRENCY", DEFAULT_MAX_CONCURRENCY)
-        )
-        self._default_batch_size = int(
-            os.environ.get("DEFAULT_BATCH_SIZE", DEFAULT_BATCH_SIZE)
-        )
+        self.request_metadata = None
+        
+class DummyRequest:
+    def __init__(self):
+        self.headers = {}
+        self.state = DummyState()
+    async def is_disconnected(self):
+        return False

-    @property
-    def max_concurrency(self):
-        return self._max_concurrency
-
-    @property
-    def default_batch_size(self):
-        return self._default_batch_size
-
-
-def validate_sampling_params(params: Dict[str, Any]) -> SamplingParams:
-    validated_params = {}
-
-    for key, value in params.items():
-        expected_type = sampling_param_types.get(key)
-        if value is None:
-            validated_params[key] = None
-            continue
-
-        if expected_type is None:
-            continue
-
-        if isinstance(expected_type, tuple):
-            casted_value = next(
-                (t(value) for t in expected_type if isinstance(value, t)), None
-            )
+class BatchSize:
+    def __init__(self, max_batch_size, min_batch_size, batch_size_growth_factor):
+        self.max_batch_size = max_batch_size
+        self.batch_size_growth_factor = batch_size_growth_factor
+        self.min_batch_size = min_batch_size
+        self.is_dynamic = batch_size_growth_factor > 1 and min_batch_size >= 1 and max_batch_size > min_batch_size
+        if self.is_dynamic:
+            self.current_batch_size = min_batch_size
        else:
-            casted_value = value if isinstance(value, expected_type) else None
+            self.current_batch_size = max_batch_size
+        
+    def update(self):
+        if self.is_dynamic:
+            self.current_batch_size = min(self.current_batch_size*self.batch_size_growth_factor, self.max_batch_size)
+        
+def create_error_response(message: str, err_type: str = "BadRequestError", status_code: HTTPStatus = HTTPStatus.BAD_REQUEST) -> ErrorResponse:
+    return ErrorResponse(error=ErrorInfo(message=message,
+                                         type=err_type,
+                                         code=status_code.value))
+    
+def get_int_bool_env(env_var: str, default: bool) -> bool:
+    return int(os.getenv(env_var, int(default))) == 1

-        if casted_value is not None:
-            validated_params[key] = casted_value
-
-    return SamplingParams(**validated_params)
+def timer_decorator(func):
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        start = time()
+        result = func(*args, **kwargs)
+        end = time()
+        logging.info(f"{func.__name__} completed in {end - start:.2f} seconds")
+        return result
+    return wrapper