Update README.md for 0.3.2

feat: auto build cuda version

.example-testenv

@@ -1,3 +0,0 @@
-MODEL_NAME="mistralai/Mistral-7B-Instruct-v0.1"
-MODEL_BASE_PATH="./models"
-DISABLE_LOG_STATS=0

.github/workflows/docker-build-release.yml

@@ -19,6 +19,10 @@ jobs:
     # 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.
 
+    strategy:
+      matrix:
+        cuda_version: [11.8.0, 12.1.0]
+
     steps:
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v2
@@ -37,4 +41,5 @@ jobs:
         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' }}
+          tags: ${{ vars.DOCKERHUB_REPO }}/${{ vars.DOCKERHUB_IMG }}:${{ (github.event_name == 'release' && github.event.release.tag_name) || (github.event_name == 'workflow_dispatch' && github.event.inputs.image_tag) || 'dev' }}-cuda${{ matrix.cuda_version }}
+          build-args: WORKER_CUDA_VERSION=${{ matrix.cuda_version }}

.gitignore

@@ -2,4 +2,5 @@
 runpod.toml
 *.pyc
 .env
-test/*
+test/*
+vllm-base/vllm-*

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "vllm-base-image/vllm"]
+	path = vllm-base-image/vllm
+	url = https://github.com/runpod/vllm-fork-for-sls-worker.git

Dockerfile

@@ -1,49 +1,48 @@
-# Base image - Set default to CUDA 11.8
+ARG WORKER_CUDA_VERSION=11.8.0
-ARG WORKER_CUDA_VERSION=11.8
+FROM runpod/worker-vllm:base-0.3.2-cuda${WORKER_CUDA_VERSION} AS vllm-base
-FROM runpod/base:0.4.2-cuda${WORKER_CUDA_VERSION}.0 as builder
-
-ARG WORKER_CUDA_VERSION=11.8 # Required duplicate to keep in scope
-
-# 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 apt-get update -y \
+    && apt-get install -y python3-pip
 
 # Install Python dependencies
 COPY builder/requirements.txt /requirements.txt
 RUN --mount=type=cache,target=/root/.cache/pip \
-    python3.11 -m pip install --upgrade pip && \
+    python3 -m pip install --upgrade pip && \
-    python3.11 -m pip install --upgrade -r /requirements.txt && \
+    python3 -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 .
 
 # Setup for Option 2: Building the Image with the Model included
 ARG MODEL_NAME=""
-ARG MODEL_BASE_PATH="/runpod-volume/"
+ARG TOKENIZER_NAME=""
-ARG HF_TOKEN=""
+ARG BASE_PATH="/runpod-volume"
 ARG QUANTIZATION=""
-RUN if [ -n "$MODEL_NAME" ]; then \
+ARG MODEL_REVISION=""
-        export MODEL_BASE_PATH=$MODEL_BASE_PATH && \
+ARG TOKENIZER_REVISION=""
-        export MODEL_NAME=$MODEL_NAME && \
+
-        python3.11 /download_model.py --model $MODEL_NAME; \
+ENV MODEL_NAME=$MODEL_NAME \
+    MODEL_REVISION=$REVISION \
+    TOKENIZER_NAME=$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_TRANSFER=1 
+
+ENV PYTHONPATH="/:/vllm-installation"
+
+COPY builder/download_model.py /download_model.py
+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 \
+    if [ -n "$MODEL_NAME" ]; then \
-        export QUANTIZATION=$QUANTIZATION; \
+        python3 /download_model.py; \
     fi
 
+# Add source files
+COPY src /src
+
+
 # Start the handler
-CMD ["python3.11", "/handler.py"]
+CMD ["python3", "/src/handler.py"]

README.md

@@ -1,137 +1,518 @@
 <div align="center">
 
-<h1>vLLM 0.2.6 Endpoint | Serverless Worker </h1>
+<h1> vLLM Serverless Endpoint Worker </h1>
+
+Deploy Blazing-fast LLMs powered by [vLLM](https://github.com/vllm-project/vllm) on RunPod Serverless in a few clicks.
+
+<p>Worker Version: 0.3.2 | vLLM Version: 0.3.3</p>
 
 [![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
+### Worker vLLM 0.3.0: What's New since 0.2.0:
+- **🚀 Full OpenAI Compatibility 🚀**
+
+  You may now use your deployment with any OpenAI Codebase by changing **only 3 lines** in total. The supported routes are <ins>Chat Completions</ins>, <ins>Completions</ins>, and <ins>Models</ins> - with both streaming and non-streaming.
+- **Dynamic Batch Size** - time-to-first token as fast no batching, while maintaining the performance of batched token streaming throughout the request.
+- vLLM 0.2.7 -> 0.3.2
+  - Gemma, DeepSeek MoE and OLMo support.
+  - FP8 KV Cache support
+  - New supported parameters
+  - We're working on adding support for Multi-LoRA ⚙️
+- Support for a wide range of new settings for your endpoint, such as Custom chat templates.
+- Fixed Tensor Parallelism, baking model into images, and more bugs.
+- Refactors and general improvements.
+
+## Table of Contents
+- [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)
+    - [Prerequisites](#prerequisites)
+    - [Environment Variables](#environment-variables)
+      - [LLM Settings](#llm-settings)
+      - [Tokenizer Settings](#tokenizer-settings)
+      - [Tensor Parallelism (Multi-GPU) Settings](#tensor-parallelism-multi-gpu-settings)
+      - [System Settings](#system-settings)
+      - [Streaming Batch Size](#streaming-batch-size)
+      - [OpenAI Settings](#openai-settings)
+      - [Serverless Settings](#serverless-settings)
+  - [Option 2: Build Docker Image with Model Inside](#option-2-build-docker-image-with-model-inside)
+    - [Prerequisites](#prerequisites-1)
+    - [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](#chat-completions)
+      - [Completions](#completions)
+    - [Examples: Using your RunPod endpoint with OpenAI](#examples-using-your-runpod-endpoint-with-openai)
+- [Usage: standard](#non-openai-usage)
+  - [Input Request Parameters](#input-request-parameters)
+    - [Text Input Formats](#text-input-formats)
+    - [Sampling Parameters](#sampling-parameters)
+
+# Setting up the Serverless Worker
+
+### Option 1: Deploy Any Model Using Pre-Built Docker Image [Recommended]
+> [!TIP]
+> This is the quickest and easiest way to tes your model, as it does not require you to build a Docker image, upload heavy models to DockerHub and wait for workers to download them. You can use this option to deploy your model in a few clicks. For even more convenience, attach a network storage volume to your Endpoint, which will download the model once and share it across all workers.
+> 
+> However, for actual deployment, it is recommended that you build an image with the model baked in, which is described in Option 2 - this will ensure the fastest load speeds.
 
-### 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```
+## RunPod Worker Images
 
-</div>
+Below is a summary of the available RunPod Worker images, categorized by image stability and CUDA version compatibility.
+
+| CUDA Version | Stable Image Tag                  | Development Image Tag             | Note                                                        |
+|--------------|-----------------------------------|-----------------------------------|----------------------------------------------------------------------|
+| 11.8.0       | `runpod/worker-vllm:0.3.2-cuda11.8.0`        | `runpod/worker-vllm:dev-cuda11.8.0`   | Available on all RunPod Workers without additional selection needed. |
+| 12.1.0       | `runpod/worker-vllm:0.3.2-cuda12.1.0` | `runpod/worker-vllm:dev-cuda12.1.0` | When creating an Endpoint, select CUDA Version 12.2 and 12.1 in the filter. |
+
+This table provides a quick reference to the image tags you should use based on the desired CUDA version and image stability (Stable or Development). Ensure to follow the selection note for CUDA 12.1.0 compatibility.
+
+---
+
+#### Prerequisites
+- RunPod Account
 
 #### Environment Variables
-- **Required**:
+> Note:  `0` is equivalent to `False` and `1` is equivalent to `True` for boolean values.
-   - `MODEL_NAME`: Hugging Face Model Repository (e.g., `openchat/openchat-3.5-1210`).
+
+| Name                                | Default              | Type/Choices                              | Description |
+|-------------------------------------|----------------------|-------------------------------------------|-------------|
+**LLM Settings**
+| `MODEL_NAME`**\***                        | -                    | `str`                                         | Hugging Face Model Repository (e.g., `openchat/openchat-3.5-1210`). |
+| `MODEL_REVISION`                    | `None`               | `str`                                         |Model revision(branch) to load. |
+| `MAX_MODEL_LEN`                  | Model's maximum      | `int`                                         |Maximum number of tokens for the engine to handle per request. |
+| `BASE_PATH`                         | `/runpod-volume`     | `str`                                         |Storage directory for Huggingface cache and model. Utilizes network storage if attached when pointed at `/runpod-volume`, which will have only one worker download the model once, which all workers will be able to load. If no network volume is present, creates a local directory within each worker. |
+| `LOAD_FORMAT`                       | `auto`               | `str`                                         |Format to load model in. |
+| `HF_TOKEN`                          | -                    | `str`                                         |Hugging Face token for private and gated models. |
+| `QUANTIZATION`                      | `None`               | `awq`, `squeezellm`, `gptq`              |Quantization of given model. The model must already be quantized. |
+| `TRUST_REMOTE_CODE`                 | `0`                  | boolean as `int`                                         |Trust remote code for Hugging Face models. Can help with Mixtral 8x7B, Quantized models, and unusual models/architectures.
+| `SEED`                              | `0`                  | `int`                                         |Sets random seed for operations. |
+| `KV_CACHE_DTYPE`                    | `auto`               | boolean as `int`                                         |Data type for kv cache storage. Uses `DTYPE` if set to `auto`. |
+| `DTYPE`                             | `auto`               | `auto`, `half`, `float16`, `bfloat16`, `float`, `float32` |Sets datatype/precision for model weights and activations. |
+**Tokenizer Settings**
+| `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) |
+**System, GPU, and Tensor Parallelism(Multi-GPU) Settings**
+| `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`                     | `0`                  | boolean as `int`                                         |Always use eager-mode PyTorch. If False(`0`), will use eager mode and CUDA graph in hybrid for maximal performance and flexibility. |
+| `MAX_CONTEXT_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. |
+**Streaming Batch Size Settings**:  
+| `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. |
+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 |
+**OpenAI Settings**
+| `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. |
+**Serverless Settings**
+| `MAX_CONCURRENCY`                   | `300`                | `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`                 | `1`                  | boolean as `int`                                         |Enables or disables vLLM stats logging. |
+| `DISABLE_LOG_REQUESTS`              | `1`                  | boolean as `int`                                         |Enables or disables vLLM request logging. |
+
+> [!TIP]
+> If you are facing issues when using Mixtral 8x7B, Quantized models, or handling unusual models/architectures, try setting `TRUST_REMOTE_CODE` to `1`.
 
-- **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.
 
 ### 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.
+To build an image with the model baked in, you must specify the following docker arguments when building the image.
 
-To build an image with the model baked in, you must specify the following docker arguments when building the image:
+#### Prerequisites
+- RunPod Account
+- Docker
 
 #### 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`: `11.8.0` or `12.1.0` (default: `11.8.0` due to a small number of workers not having CUDA 12.1 support yet. `12.1.0` is recommended for optimal performance).
-  - `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).
+  - `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`).
+
+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.
 
 #### 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" .`
+```bash
+sudo docker build -t username/image:tag --build-arg MODEL_NAME="openchat/openchat_3.5" --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
+Below are all supported model architectures (and examples of each) that you can deploy using the vLLM Worker. You can deploy **any model on HuggingFace**, as long as its base architecture is one of the following:
 
-### 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.)
+- DeciLM (`Deci/DeciLM-7B`, `Deci/DeciLM-7B-instruct`, etc.)
 - Falcon (`tiiuae/falcon-7b`, `tiiuae/falcon-40b`, `tiiuae/falcon-rw-7b`, etc.)
+- Gemma (`google/gemma-2b`, `google/gemma-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.)
+- InternLM2 (`internlm/internlm2-7b`, `internlm/internlm2-chat-7b`, etc.)
+- 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.)
 - MPT (`mosaicml/mpt-7b`, `mosaicml/mpt-30b`, etc.)
+- OLMo (`allenai/OLMo-1B`, `allenai/OLMo-7B`, 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.)
+- Qwen2 (`Qwen/Qwen2-7B-beta`, `Qwen/Qwen-7B-Chat-beta`, etc.)
+- StableLM(`stabilityai/stablelm-3b-4e1t`, `stabilityai/stablelm-base-alpha-7b-v2`, etc.)
 - Yi (`01-ai/Yi-6B`, `01-ai/Yi-34B`, etc.)
-  
-And any other models supported by vLLM 0.2.6.
 
+# 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>, <ins>Completions</ins> and <ins>Models</ins> - with both streaming and non-streaming.
 
-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.
+## 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
 
-## Model Inputs
+    client = OpenAI(
-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`.
+        api_key=os.environ.get("RUNPOD_API_KEY"),
-| Argument        | Type | Default            | Description                                                                                   |
+        base_url="https://api.runpod.ai/v2/<YOUR ENDPOINT ID>/openai/v1",
-|-----------------|------|--------------------|-----------------------------------------------------------------------------------------------|
+    )
-| `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`.                                                 |
+2. Change the `model` parameter to your deployed model's name whenever using Completions or Chat Completions.
-| `apply_chat_template`       | bool | False              | Whether to apply the model's chat template to the `prompt`. |
+    - Before:
-| `sampling_params` | dict | {}                 | Sampling parameters to control the generation, like temperature, top_p, etc.                  |
+    ```python
-| `stream`       | bool | False              | Whether to enable streaming of output. If True, responses are streamed as they are generated. |
+    response = client.chat.completions.create(
-| `batch_size`      | int  | DEFAULT_BATCH_SIZE | The number of tokens to stream every HTTP POST call.                            |
+        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,
+    )
+    ```
 
-### Messages Format
+**Using http requests**:
-Your list can contain any number of messages, and each message can have any role from the following list:
+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`
-- `user`
+    - Before:
-- `assistant`
+    ```bash
-- `system`
+    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
+    }'
+    ```
 
-The model's chat template will be applied to the messages automatically. 
+## OpenAI Request Input Parameters:
 
-Example:
+When using the chat completion feature of the vLLM Serverless Endpoint Worker, you can customize your requests with the following parameters:
-```json
+
-[
+### Chat Completions
-  {
+<details>
-    "role": "system",
+  <summary>Supported Chat Completions Inputs and Descriptions</summary>
-    "content": "..."
+
-  },
+  | Parameter                      | Type                             | Default Value | Description                                                                                                                                                                                                                                                                                           |
-  {
+  |--------------------------------|----------------------------------|---------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-    "role": "user",
+  | `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.                                                                                                                        |
-    "content": "..."
+  | `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. |
-    "role": "assistant",
+  | `n`                            | Optional[int]                    | 1             | Number of output sequences to return for the given prompt. |
-    "content": "..."
+  | `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>
+
+### Completions
+<details>
+  <summary>Supported Completions Inputs and Descriptions</summary>
+
+  | Parameter                      | Type                             | Default Value | Description                                                                                                                                                                                                                                                                                           |
+  |--------------------------------|----------------------------------|---------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | `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.                                                                                                                                                                                                                                                    |
+  | `prompt`                       | Union[List[int], List[List[int]], str, List[str]] | | A string, array of strings, array of tokens, or array of token arrays to be used as the input for the model. |
+  | `suffix`                       | Optional[str]                    | None          | A string to be appended to the end of the generated text. |
+  | `max_tokens`                   | Optional[int]                    | 16            | Maximum number of tokens to generate per output sequence. |
+  | `temperature`                  | Optional[float]                  | 1.0           | 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. |
+  | `stream`                       | Optional[bool]                   | False         | Whether to stream the output. |
+  | `logprobs`                     | Optional[int]                    | None          | Number of log probabilities to return per output token. |
+  | `echo`                         | Optional[bool]                   | False         | Whether to echo back the prompt in addition to the completion. |
+  | `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. |
+  | `seed`                         | Optional[int]                    | None          | Random seed to use for the generation. |
+  | `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. |
+  | `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 parameter influences the diversity of the output. |
+  | `logit_bias`                   | Optional[Dict[str, float]]       | None          | Dictionary of token IDs to biases. |
+  | `user`                         | Optional[str]                    | None          | User identifier for personalizing responses. (Unsupported by vLLM) |
+  Additional parameters supported by vLLM:
+  | `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 End Of Sentence 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 for generating outputs. |
+  | `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. |
+  | `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 be considered, relative to the most likely token. Must be in [0, 1]. Set to 0 to disable. |
+  | `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",
+)
 ```
 
-### Sampling Parameters
+### Chat Completions:
-| Argument                      | Type                        | Default | Description                                                                                                                                                                                   |
+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**:
-| `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`. |
+  ```python
-| `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.                                                  |
+  # Create a chat completion stream
-| `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.                                                 |
+  response_stream = client.chat.completions.create(
-| `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.                                            |
+      model="<YOUR DEPLOYED MODEL REPO/NAME>",
-| `temperature`                   | float                       | 1.0     | Controls the randomness of sampling. Lower values make it more deterministic, higher values make it more random. Zero means greedy sampling.                                                  |
+      messages=[{"role": "user", "content": "Why is RunPod the best platform?"}],
-| `top_p`                         | float                       | 1.0     | Controls the cumulative probability of top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.                                                                            |
+      temperature=0,
-| `top_k`                         | int                         | -1      | Controls the number of top tokens to consider. Set to -1 to consider all tokens.                                                                                                              |
+      max_tokens=100,
-| `min_p`                         | float                       | 0.0     | Represents the minimum probability for a token to be considered, relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.                                                   |
+      stream=True,
-| `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.                                                                                                                               |
+  # Stream the response
-| `early_stopping`                | Union[bool, str]            | False   | Controls stopping condition in beam search. Can be `True`, `False`, or `"never"`.                                                                                                             |
+  for response in response_stream:
-| `stop`                          | Union[None, str, List[str]] | None    | List of strings that stop generation when produced. Output will not contain these strings.                                                                                                    |
+      print(chunk.choices[0].delta.content or "", end="", flush=True)
-| `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.                                                                                              |
+- **Non-Streaming**:
-| `max_tokens`                    | int                         | 16      | Maximum number of tokens to generate per output sequence.                                                                                                                                     |
+  ```python
-| `skip_special_tokens`           | bool                        | True    | Whether to skip special tokens in the output.                                                                                                                                                 |
+  # Create a chat completion
-| `spaces_between_special_tokens` | bool                        | True    | Whether to add spaces between special tokens in the output.                                                                                                                                   |
+  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)
+  ```
+
+
+### Completions:
+This is the format used for models like GPT-3 and is meant for completing the text you provide. Instead of responding to your message, it will try to complete it. Examples of Open Source completions models include `meta-llama/Llama-2-7b-hf`, `mistralai/Mixtral-8x7B-v0.1`, `Qwen/Qwen-72B`, and more. However, you can use any model with this format.
+- **Streaming**:
+  ```python
+  # Create a completion stream
+  response_stream = client.completions.create(
+      model="<YOUR DEPLOYED MODEL REPO/NAME>",
+      prompt="Runpod is the best platform because",
+      temperature=0,
+      max_tokens=100,
+      stream=True,
+  )
+  # Stream the response
+  for response in response_stream:
+      print(response.choices[0].text or "", end="", flush=True)
+  ```
+- **Non-Streaming**:
+  ```python
+  # Create a completion
+  response = client.completions.create(
+      model="<YOUR DEPLOYED MODEL REPO/NAME>",
+      prompt="Runpod is the best platform because",
+      temperature=0,
+      max_tokens=100,
+  )
+  # Print the response
+  print(response.choices[0].text)
+  ```
+
+### 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
+
+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.                                            |
+  | `temperature`                   | float                       | 1.0     | Controls the randomness of sampling. Lower values make it more deterministic, higher values make it more random. Zero means greedy sampling.                                                  |
+  | `top_p`                         | float                       | 1.0     | Controls the cumulative probability of top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.                                                                            |
+  | `top_k`                         | int                         | -1      | Controls the number of top tokens to consider. Set to -1 to consider all tokens.                                                                                                              |
+  | `min_p`                         | float                       | 0.0     | Represents the minimum probability for a token to be considered, relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.                                                   |
+  | `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. 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
+    "prompt": "..."
+    ```
+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
+    "messages": [
+        {
+          "role": "system",
+          "content": "..."
+        },
+        {
+          "role": "user",
+          "content": "..."
+        },
+        {
+          "role": "assistant",
+          "content": "..."
+        }
+      ]
+    ```
 

builder/download_model.py

@@ -0,0 +1,50 @@
+import os
+import shutil
+from huggingface_hub import snapshot_download
+from vllm.model_executor.weight_utils import prepare_hf_model_weights, Disabledtqdm
+
+def download_extras_or_tokenizer(model_name, cache_dir, revision, extras=False):
+    """Download model or tokenizer and prepare its weights, returning the local folder path."""
+    pattern = ["*token*", "*.json"] if extras else None
+    extra_dir = "/extras" if extras else ""
+    folder = snapshot_download(
+        model_name,
+        cache_dir=cache_dir + extra_dir,
+        revision=revision,
+        tqdm_class=Disabledtqdm,
+        allow_patterns=pattern if extras else None,
+        ignore_patterns=["*.safetensors", "*.bin", "*.pt"] if not extras else None
+    )
+    return folder
+
+def move_files(src_dir, dest_dir):
+    """Move files from source to destination directory."""
+    for f in os.listdir(src_dir):
+        src_path = os.path.join(src_dir, f)  
+        dst_path = os.path.join(dest_dir, f)         
+        shutil.copy2(src_path, dst_path)
+        os.remove(src_path)
+        
+if __name__ == "__main__":
+    model, download_dir = os.getenv("MODEL_NAME"), os.getenv("HF_HOME")
+    tokenizer = os.getenv("TOKENIZER_NAME") or model
+
+    revisions = {
+        "model": os.getenv("MODEL_REVISION") or None,
+        "tokenizer": os.getenv("TOKENIZER_REVISION") or None
+    }
+
+    if not model or not download_dir:
+        raise ValueError(f"Must specify model and download_dir. Model: {model}, download_dir: {download_dir}")
+
+    os.makedirs(download_dir, exist_ok=True)
+    model_folder, hf_weights_files, use_safetensors = prepare_hf_model_weights(model_name_or_path=model, revision=revisions["model"], cache_dir=download_dir)
+    model_extras_folder = download_extras_or_tokenizer(model, download_dir, revisions["model"], extras=True)
+    move_files(model_extras_folder, model_folder)
+
+    with open("/local_model_path.txt", "w") as f:
+        f.write(model_folder)
+
+    tokenizer_folder = download_extras_or_tokenizer(tokenizer, download_dir, revisions["tokenizer"])
+    with open("/local_tokenizer_path.txt", "w") as f:
+        f.write(tokenizer_folder)

builder/requirements.txt

@@ -1,6 +1,10 @@
 hf_transfer
-runpod==1.5.2
+ray
+pandas
+pyarrow
+runpod==1.6.2
 huggingface-hub
 packaging
 typing-extensions==4.7.1
 pydantic
+pydantic-settings

src/config.py

@@ -0,0 +1,53 @@
+import os
+from dotenv import load_dotenv
+from torch.cuda import device_count
+import os
+
+class EngineConfig:
+    def __init__(self):
+        load_dotenv()
+        self.model_name_or_path, self.hf_home, self.model_revision = self._get_local_or_env("/local_model_path.txt", "MODEL_NAME")
+        self.tokenizer_name_or_path, _, self.tokenizer_revision = self._get_local_or_env("/local_tokenizer_path.txt", "TOKENIZER_NAME")
+        self.tokenizer_name_or_path = self.tokenizer_name_or_path or self.model_name_or_path
+        self.quantization = self._get_quantization()
+        self.config = self._initialize_config()
+
+    def _get_local_or_env(self, local_path, env_var):
+        if os.path.exists(local_path):
+            os.environ["TRANSFORMERS_OFFLINE"] = "1"
+            os.environ["HF_HUB_OFFLINE"] = "1"
+            with open(local_path, "r") as file:
+                return file.read().strip(), None, None
+        return os.getenv(env_var), os.getenv("HF_HOME"), os.getenv(f"{env_var.split('_')[0]}_REVISION") or None
+
+    def _get_quantization(self):
+        quantization = os.getenv("QUANTIZATION", "").lower()
+        return quantization if quantization in ["awq", "squeezellm", "gptq"] else None
+
+    def _initialize_config(self):
+        args = {
+            "model": self.model_name_or_path,
+            "revision": self.model_revision,
+            "download_dir": self.hf_home,
+            "quantization": self.quantization,
+            "load_format": os.getenv("LOAD_FORMAT", "auto"),
+            "dtype": os.getenv("DTYPE", "half" if self.quantization else "auto"),
+            "tokenizer": self.tokenizer_name_or_path,
+            "tokenizer_revision": self.tokenizer_revision,
+            "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", 0.95)),
+            "max_parallel_loading_workers": None if device_count() > 1 or not os.getenv("MAX_PARALLEL_LOADING_WORKERS") else int(os.getenv("MAX_PARALLEL_LOADING_WORKERS")),
+            "max_model_len": int(os.getenv("MAX_MODEL_LEN")) if os.getenv("MAX_MODEL_LEN") else None,
+            "tensor_parallel_size": device_count(),
+            "seed": int(os.getenv("SEED")) if os.getenv("SEED") else None,
+            "kv_cache_dtype": os.getenv("KV_CACHE_DTYPE"),
+            "block_size": int(os.getenv("BLOCK_SIZE")) if os.getenv("BLOCK_SIZE") else None,
+            "swap_space": int(os.getenv("SWAP_SPACE")) if os.getenv("SWAP_SPACE") else None,
+            "max_context_len_to_capture": int(os.getenv("MAX_CONTEXT_LEN_TO_CAPTURE")) if os.getenv("MAX_CONTEXT_LEN_TO_CAPTURE") else None,
+            "disable_custom_all_reduce": bool(int(os.getenv("DISABLE_CUSTOM_ALL_REDUCE", 0))),
+            "enforce_eager": bool(int(os.getenv("ENFORCE_EAGER", 0)))
+        }
+        
+        return {k: v for k, v in args.items() if v is not None}

src/constants.py

@@ -1,25 +1,4 @@
-DEFAULT_BATCH_SIZE = 30
+DEFAULT_BATCH_SIZE = 50
 DEFAULT_MAX_CONCURRENCY = 300
-
+DEFAULT_BATCH_SIZE_GROWTH_FACTOR = 3
-sampling_param_types = {
+DEFAULT_MIN_BATCH_SIZE = 1
-    "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,
-}

src/download_model.py

@@ -1,22 +0,0 @@
-import argparse
-import os
-from vllm.model_executor.weight_utils import prepare_hf_model_weights
-
-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,
-    )

src/engine.py

@@ -1,55 +1,102 @@
 import os
 import logging
-from typing import Union
+import json
-import torch
+
-from vllm import AsyncLLMEngine, AsyncEngineArgs
-from transformers import AutoTokenizer
-from utils import ServerlessConfig
 from dotenv import load_dotenv
+from torch.cuda import device_count
+from typing import AsyncGenerator
 
+from vllm import AsyncLLMEngine, AsyncEngineArgs
+from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
+from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
+from vllm.entrypoints.openai.protocol import ChatCompletionRequest, CompletionRequest, ErrorResponse
 
-class Tokenizer:
+from utils import DummyRequest, JobInput, BatchSize, create_error_response
-    def __init__(self, model_name: str):
+from constants import DEFAULT_MAX_CONCURRENCY, DEFAULT_BATCH_SIZE, DEFAULT_BATCH_SIZE_GROWTH_FACTOR, DEFAULT_MIN_BATCH_SIZE
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+from tokenizer import TokenizerWrapper
-        self.has_chat_template = bool(self.tokenizer.chat_template)
+from config import EngineConfig
-
-    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
-        )
-
 
 class vLLMEngine:
-    def __init__(self):
+    def __init__(self, engine = None):
         load_dotenv() # For local development
-        self.config = self._initialize_config()
+        self.config = EngineConfig().config
-        self.serverless_config = ServerlessConfig()
+        self.tokenizer = TokenizerWrapper(self.config.get("tokenizer"), self.config.get("tokenizer_revision"), self.config.get("trust_remote_code"))
-        self.tokenizer = Tokenizer(self.config["model"])
+        self.llm = self._initialize_llm() if engine is None else engine
-        self.llm = self._initialize_llm()
+        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):
+    def dynamic_batch_size(self, current_batch_size, batch_size_growth_factor):
-        return {
+        return min(current_batch_size*batch_size_growth_factor, self.default_batch_size)
-            "model": os.getenv("MODEL_NAME"),
+                           
-            "download_dir": os.getenv("MODEL_BASE_PATH", "/runpod-volume/"),
+    async def generate(self, job_input: JobInput):
-            "quantization": self._get_quantization(),
+        try:
-            "dtype": "auto" if os.getenv("QUANTIZATION") is None else "half",
+            async for batch in self._generate_vllm(
-            "disable_log_stats": bool(int(os.getenv("DISABLE_LOG_STATS", 1))),
+                llm_input=job_input.llm_input,
-            "disable_log_requests": bool(int(os.getenv("DISABLE_LOG_REQUESTS", 1))),
+                validated_sampling_params=job_input.sampling_params,
-            "trust_remote_code": bool(int(os.getenv("TRUST_REMOTE_CODE", 0))),
+                batch_size=job_input.max_batch_size,
-            "gpu_memory_utilization": float(os.getenv("GPU_MEMORY_UTILIZATION", 0.98)),
+                stream=job_input.stream,
-            "max_model_len": self._get_max_model_len(),
+                apply_chat_template=job_input.apply_chat_template,
-            "tensor_parallel_size": self._get_num_gpu_shard(),
+                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):
+            llm_input = self.tokenizer.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:
@@ -57,33 +104,84 @@ class vLLMEngine:
         except Exception as e:
             logging.error("Error initializing vLLM engine: %s", e)
             raise e
+
+
+class OpenAIvLLMEngine:
+    def __init__(self, vllm_engine):
+        self.config = vllm_engine.config
+        self.llm = vllm_engine.llm
+        self.served_model_name = os.getenv("OPENAI_SERVED_MODEL_NAME_OVERRIDE") or self.config["model"]
+        self.response_role = os.getenv("OPENAI_RESPONSE_ROLE") or "assistant"
+        self.tokenizer = vllm_engine.tokenizer
+        self.default_batch_size = vllm_engine.default_batch_size
+        self.batch_size_growth_factor, self.min_batch_size = vllm_engine.batch_size_growth_factor, vllm_engine.min_batch_size
+        self._initialize_engines()
+        self.raw_openai_output = bool(int(os.getenv("RAW_OPENAI_OUTPUT", 1)))
+
+    def _initialize_engines(self):
+        self.chat_engine = OpenAIServingChat(
+            self.llm, self.served_model_name, self.response_role,
+            chat_template=self.tokenizer.tokenizer.chat_template
+        )
+        self.completion_engine = OpenAIServingCompletion(self.llm, self.served_model_name)
+    
+    async def generate(self, openai_request: JobInput):
+        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.chat_engine.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
         
-    def _get_num_gpu_shard(self):
+        try:
-        final_num_gpu_shard = 1
+            request = request_class(
-        if bool(int(os.getenv("USE_TENSOR_PARALLEL", 0))):
+                **openai_request.openai_input
-            env_num_gpu_shard = int(os.getenv("TENSOR_PARALLEL_SIZE", 1))
+            )
-            num_gpu_available = torch.cuda.device_count()
+        except Exception as e:
-            final_num_gpu_shard = min(env_num_gpu_shard, num_gpu_available)
+            yield create_error_response(str(e)).model_dump()
-            logging.info("Using %s GPU shards", final_num_gpu_shard)
+            return
-        return final_num_gpu_shard
+        
-    
+        response_generator = await generator_function(request, DummyRequest())
-    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
-
-    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 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
+            

src/handler.py

@@ -1,67 +1,22 @@
-#!/usr/bin/env python
+import os
-from typing import Generator
 import runpod
-from utils import validate_sampling_params, random_uuid
+from utils import JobInput
-from engine import vLLMEngine
+from engine import vLLMEngine, OpenAIvLLMEngine
 
 vllm_engine = vLLMEngine()
-async def handler(job: dict) -> Generator[dict, None, None]:
+OpenAIvLLMEngine = OpenAIvLLMEngine(vllm_engine)
-    job_input = job["input"]
-    llm_input = job_input.get("messages", job_input.get("prompt"))
-    apply_chat_template = job_input.get("apply_chat_template", False)
 
-    if apply_chat_template or isinstance(llm_input, list):
+async def handler(job):
-        llm_input = vllm_engine.tokenizer.apply_chat_template(llm_input)
+    job_input = JobInput(job["input"])
-
+    engine = OpenAIvLLMEngine if job_input.openai_route else vllm_engine
-    stream = job_input.get("stream", False)
+    results_generator = engine.generate(job_input)
-    batch_size = job_input.get("batch_size", vllm_engine.serverless_config.default_batch_size)
+    async for batch in results_generator:
-    sampling_params = job_input.get("sampling_params", {})
+        yield batch
-
-    validated_params = validate_sampling_params(sampling_params)
-    request_id = random_uuid()
-    results_generator = vllm_engine.llm.generate(
-        llm_input, validated_params, request_id
-    )
-
-    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,
+        "concurrency_modifier": lambda x: vllm_engine.max_concurrency,
         "return_aggregate_stream": True,
     }
-)
+)

src/tokenizer.py

@@ -0,0 +1,26 @@
+from transformers import AutoTokenizer
+import os
+from typing import Union
+
+class TokenizerWrapper:
+    def __init__(self, tokenizer_name_or_path, tokenizer_revision, trust_remote_code):
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, revision=tokenizer_revision, 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
+        )

src/utils.py

@@ -1,51 +1,68 @@
-import os
 import logging
+from http import HTTPStatus
 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 vllm.entrypoints.openai.protocol import ErrorResponse
+from vllm import SamplingParams
 
 logging.basicConfig(level=logging.INFO)
 
+def count_physical_cores():
+    with open('/proc/cpuinfo') as f:
+        content = f.readlines()
 
-class ServerlessConfig:
+    cores = set()
-    def __init__(self):
+    current_physical_id = None
-        self._max_concurrency = int(
+    current_core_id = None
-            os.environ.get("MAX_CONCURRENCY", DEFAULT_MAX_CONCURRENCY)
-        )
-        self._default_batch_size = int(
-            os.environ.get("DEFAULT_BATCH_SIZE", DEFAULT_BATCH_SIZE)
-        )
 
-    @property
+    for line in content:
-    def max_concurrency(self):
+        if 'physical id' in line:
-        return self._max_concurrency
+            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))
 
-    @property
+    return len(cores)
-    def default_batch_size(self):
-        return self._default_batch_size
 
 
-def validate_sampling_params(params: Dict[str, Any]) -> SamplingParams:
+class JobInput:
-    validated_params = {}
+    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)
+        self.sampling_params = SamplingParams(**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")
 
-    for key, value in params.items():
+class DummyRequest:
-        expected_type = sampling_param_types.get(key)
+    async def is_disconnected(self):
-        if value is None:
+        return False
-            validated_params[key] = None
+    
-            continue
+class BatchSize:
-
+    def __init__(self, max_batch_size, min_batch_size, batch_size_growth_factor):
-        if expected_type is None:
+        self.max_batch_size = max_batch_size
-            continue
+        self.batch_size_growth_factor = batch_size_growth_factor
-
+        self.min_batch_size = min_batch_size
-        if isinstance(expected_type, tuple):
+        self.is_dynamic = batch_size_growth_factor > 1 and min_batch_size >= 1 and max_batch_size > min_batch_size
-            casted_value = next(
+        if self.is_dynamic:
-                (t(value) for t in expected_type if isinstance(value, t)), None
+            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
-
+        
-        if casted_value is not None:
+    def update(self):
-            validated_params[key] = casted_value
+        if self.is_dynamic:
-
+            self.current_batch_size = min(self.current_batch_size*self.batch_size_growth_factor, self.max_batch_size)
-    return SamplingParams(**validated_params)
+        
+def create_error_response(message: str, err_type: str = "BadRequestError", status_code: HTTPStatus = HTTPStatus.BAD_REQUEST) -> ErrorResponse:
+    return ErrorResponse(message=message,
+                            type=err_type,
+                            code=status_code.value)
+    
+    

vllm-base-image/Dockerfile

@@ -0,0 +1,88 @@
+################### vLLM Base Dockerfile ###################
+# This Dockerfile is for building the image that the  
+# vLLM worker container will use as its base image. 
+# If your changes are outside of the vLLM source code, you
+# do not need to build this image.
+##########################################################
+
+# Define the CUDA version for the build
+ARG WORKER_CUDA_VERSION=11.8.0
+
+FROM nvidia/cuda:${WORKER_CUDA_VERSION}-devel-ubuntu22.04 AS dev
+
+# Re-declare ARG after FROM
+ARG WORKER_CUDA_VERSION
+
+# Update and install dependencies
+RUN apt-get update -y \
+    && apt-get install -y python3-pip git
+
+# Set working directory
+WORKDIR /vllm-installation
+
+# Install build and runtime dependencies
+COPY vllm/requirements-${WORKER_CUDA_VERSION}.txt  requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install -r requirements.txt
+
+# Install development dependencies
+COPY vllm/requirements-dev.txt requirements-dev.txt
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install -r requirements-dev.txt
+
+FROM dev AS build
+
+# Re-declare ARG after FROM
+ARG WORKER_CUDA_VERSION
+
+# Install build dependencies
+COPY vllm/requirements-build.txt requirements-build.txt
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install -r requirements-build.txt
+
+# Copy necessary files
+COPY vllm/csrc csrc
+COPY vllm/setup.py setup.py
+COPY vllm/pyproject.toml pyproject.toml
+COPY vllm/vllm/__init__.py vllm/__init__.py
+
+# Set environment variables for building extensions
+ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
+ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
+ARG max_jobs=48
+ENV MAX_JOBS=${max_jobs}
+ARG nvcc_threads=1024
+ENV NVCC_THREADS=${nvcc_threads}
+ENV WORKER_CUDA_VERSION=${WORKER_CUDA_VERSION}
+ENV VLLM_INSTALL_PUNICA_KERNELS=0
+# Build extensions
+RUN ldconfig /usr/local/cuda-$(echo "$WORKER_CUDA_VERSION" | sed 's/\.0$//')/compat/
+RUN python3 setup.py build_ext --inplace
+
+FROM nvidia/cuda:${WORKER_CUDA_VERSION}-runtime-ubuntu22.04 AS vllm-base
+
+# Re-declare ARG after FROM
+ARG WORKER_CUDA_VERSION
+
+# Update and install necessary libraries
+RUN apt-get update -y \
+    && apt-get install -y python3-pip
+
+# Set working directory
+WORKDIR /vllm-installation
+
+
+# Install runtime dependencies
+COPY vllm/requirements-${WORKER_CUDA_VERSION}.txt  requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install -r requirements.txt
+
+# Copy built files from the build stage
+COPY --from=build /vllm-installation/vllm/*.so /vllm-installation/vllm/
+COPY vllm/vllm vllm
+
+# Set PYTHONPATH environment variable
+ENV PYTHONPATH="/"
+
+# Validate the installation
+RUN python3 -c "import sys; print(sys.path); import vllm; print(vllm.__file__)"

vllm-base-image/README.md

@@ -0,0 +1 @@
+This directory is for building the vllm-base image utilized by the worker.