Compare commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Tim Pietrusky
|
ecd562e112 | ||
|
|
5cffaab8e8 |
+261
-260
@@ -1,260 +1,261 @@
|
||||
|
||||
|
||||
Run LLMs using [vLLM](https://docs.vllm.ai) with an OpenAI-compatible API
|
||||
|
||||
---
|
||||
|
||||
[](https://www.runpod.io/console/hub/runpod-workers/worker-vllm)
|
||||
|
||||
---
|
||||
|
||||
## Endpoint Configuration
|
||||
|
||||
All behaviour is controlled through environment variables:
|
||||
|
||||
| Environment Variable | Description | Default | Options |
|
||||
| ----------------------------------- | ------------------------------------------------- | ------------------- | ------------------------------------------------------------------ |
|
||||
| `MODEL_NAME` | Path of the model weights | "facebook/opt-125m" | Local folder or Hugging Face repo ID |
|
||||
| `HF_TOKEN` | HuggingFace access token for gated/private models | | Your HuggingFace access token |
|
||||
| `MAX_MODEL_LEN` | Model's maximum context length | | Integer (e.g., 4096) |
|
||||
| `QUANTIZATION` | Quantization method | | "awq", "gptq", "squeezellm", "bitsandbytes" |
|
||||
| `TENSOR_PARALLEL_SIZE` | Number of GPUs | 1 | Integer |
|
||||
| `GPU_MEMORY_UTILIZATION` | Fraction of GPU memory to use | 0.95 | Float between 0.0 and 1.0 |
|
||||
| `MAX_NUM_SEQS` | Maximum number of sequences per iteration | 256 | Integer |
|
||||
| `CUSTOM_CHAT_TEMPLATE` | Custom chat template override | | Jinja2 template string |
|
||||
| `ENABLE_AUTO_TOOL_CHOICE` | Enable automatic tool selection | false | boolean (true or false) |
|
||||
| `TOOL_CALL_PARSER` | Parser for tool calls | | "mistral", "hermes", "llama3_json", "granite", "deepseek_v3", etc. |
|
||||
| `OPENAI_SERVED_MODEL_NAME_OVERRIDE` | Override served model name in API | | String |
|
||||
| `MAX_CONCURRENCY` | Maximum concurrent requests | 300 | Integer |
|
||||
|
||||
For complete configuration options, see the [full configuration documentation](https://github.com/runpod-workers/worker-vllm/blob/main/docs/configuration.md).
|
||||
|
||||
## API Usage
|
||||
|
||||
This worker supports two API formats: **RunPod native** and **OpenAI-compatible**.
|
||||
|
||||
### RunPod Native API
|
||||
|
||||
For testing directly in the RunPod UI, use these examples in your endpoint's request tab.
|
||||
|
||||
#### Chat Completions
|
||||
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"messages": [
|
||||
{ "role": "system", "content": "You are a helpful assistant." },
|
||||
{ "role": "user", "content": "What is the capital of France?" }
|
||||
],
|
||||
"sampling_params": {
|
||||
"max_tokens": 100,
|
||||
"temperature": 0.7
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Chat Completions (Streaming)
|
||||
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"messages": [
|
||||
{ "role": "user", "content": "Write a short story about a robot." }
|
||||
],
|
||||
"sampling_params": {
|
||||
"max_tokens": 500,
|
||||
"temperature": 0.8
|
||||
},
|
||||
"stream": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Text Generation
|
||||
|
||||
For direct text generation without chat format:
|
||||
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"prompt": "The capital of France is",
|
||||
"sampling_params": {
|
||||
"max_tokens": 64,
|
||||
"temperature": 0.0
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### List Models
|
||||
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"openai_route": "/v1/models"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### OpenAI-Compatible API
|
||||
|
||||
For external clients and SDKs, use the `/openai/v1` path prefix with your RunPod API key.
|
||||
|
||||
#### Chat Completions
|
||||
|
||||
**Path:** `/openai/v1/chat/completions`
|
||||
|
||||
```json
|
||||
{
|
||||
"model": "meta-llama/Llama-2-7b-chat-hf",
|
||||
"messages": [
|
||||
{ "role": "system", "content": "You are a helpful assistant." },
|
||||
{ "role": "user", "content": "What is the capital of France?" }
|
||||
],
|
||||
"max_tokens": 100,
|
||||
"temperature": 0.7
|
||||
}
|
||||
```
|
||||
|
||||
#### Chat Completions (Streaming)
|
||||
|
||||
```json
|
||||
{
|
||||
"model": "meta-llama/Llama-2-7b-chat-hf",
|
||||
"messages": [
|
||||
{ "role": "user", "content": "Write a short story about a robot." }
|
||||
],
|
||||
"max_tokens": 500,
|
||||
"temperature": 0.8,
|
||||
"stream": true
|
||||
}
|
||||
```
|
||||
|
||||
#### Text Completions
|
||||
|
||||
**Path:** `/openai/v1/completions`
|
||||
|
||||
```json
|
||||
{
|
||||
"model": "meta-llama/Llama-2-7b-chat-hf",
|
||||
"prompt": "The capital of France is",
|
||||
"max_tokens": 100,
|
||||
"temperature": 0.7
|
||||
}
|
||||
```
|
||||
|
||||
#### List Models
|
||||
|
||||
**Path:** `/openai/v1/models`
|
||||
|
||||
```json
|
||||
{}
|
||||
```
|
||||
|
||||
#### Response Format
|
||||
|
||||
Both APIs return the same response format:
|
||||
|
||||
```json
|
||||
{
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"message": { "role": "assistant", "content": "Paris." },
|
||||
"finish_reason": "stop"
|
||||
}
|
||||
],
|
||||
"usage": { "prompt_tokens": 9, "completion_tokens": 1, "total_tokens": 10 }
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Usage
|
||||
|
||||
Below are minimal `python` snippets so you can copy-paste to get started quickly.
|
||||
|
||||
> Replace `<ENDPOINT_ID>` with your endpoint ID and `<API_KEY>` with a [RunPod API key](https://docs.runpod.io/get-started/api-keys).
|
||||
|
||||
### OpenAI compatible API
|
||||
|
||||
Minimal Python example using the official `openai` SDK:
|
||||
|
||||
```python
|
||||
from openai import OpenAI
|
||||
import os
|
||||
|
||||
# Initialize the OpenAI Client with your RunPod API Key and Endpoint URL
|
||||
client = OpenAI(
|
||||
api_key=os.getenv("RUNPOD_API_KEY"),
|
||||
base_url=f"https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1",
|
||||
)
|
||||
```
|
||||
|
||||
`Chat Completions (Non-Streaming)`
|
||||
|
||||
```python
|
||||
response = client.chat.completions.create(
|
||||
model="meta-llama/Llama-2-7b-chat-hf",
|
||||
messages=[{"role": "user", "content": "Explain quantum computing in simple terms"}],
|
||||
temperature=0,
|
||||
max_tokens=100,
|
||||
)
|
||||
print(f"Response: {response.choices[0].message.content}")
|
||||
```
|
||||
|
||||
`Chat Completions (Streaming)`
|
||||
|
||||
```python
|
||||
response_stream = client.chat.completions.create(
|
||||
model="meta-llama/Llama-2-7b-chat-hf",
|
||||
messages=[{"role": "user", "content": "Explain quantum computing in simple terms"}],
|
||||
temperature=0,
|
||||
max_tokens=100,
|
||||
stream=True
|
||||
)
|
||||
for response in response_stream:
|
||||
print(response.choices[0].delta.content or "", end="", flush=True)
|
||||
```
|
||||
|
||||
### RunPod Native API
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
response = requests.post(
|
||||
"https://api.runpod.ai/v2/<ENDPOINT_ID>/run",
|
||||
headers={"Authorization": "Bearer <API_KEY>"},
|
||||
json={
|
||||
"input": {
|
||||
"messages": [
|
||||
{"role": "system", "content": "You are a helpful assistant."},
|
||||
{"role": "user", "content": "Explain quantum computing in simple terms"}
|
||||
],
|
||||
"sampling_params": {
|
||||
"temperature": 0.7,
|
||||
"max_tokens": 150
|
||||
}
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
result = response.json()
|
||||
print(result["output"])
|
||||
```
|
||||
|
||||
## Compatibility
|
||||
|
||||
For supported models, see the [vLLM supported models documentation](https://docs.vllm.ai/en/latest/models/supported_models.html).
|
||||
|
||||
Anything not recognized by worker-vllm is forwarded to vLLM's engine, so advanced options in the vLLM docs (guided generation, LoRA, speculative decoding, etc.) also work.
|
||||
|
||||
## Documentation
|
||||
|
||||
- **[🚀 Deployment Guide](https://docs.runpod.io/serverless/vllm/get-started)** - Step-by-step setup
|
||||
- **[📖 Configuration Reference](https://github.com/runpod-workers/worker-vllm/blob/main/docs/configuration.md)** - All environment variables
|
||||
- **[🏗️ Advanced Deployment](https://github.com/runpod-workers/worker-vllm/blob/main/docs/deployment.md)** - Custom builds and strategies
|
||||
- **[🔧 Development Guide](https://github.com/runpod-workers/worker-vllm/blob/main/docs/conventions.md)** - Architecture and patterns
|
||||
|
||||
|
||||
Run LLMs using [vLLM](https://docs.vllm.ai) with an OpenAI-compatible API
|
||||
|
||||
---
|
||||
|
||||
[](https://www.runpod.io/console/hub/runpod-workers/worker-vllm)
|
||||
|
||||
---
|
||||
|
||||
## Endpoint Configuration
|
||||
|
||||
All behaviour is controlled through environment variables:
|
||||
|
||||
| Environment Variable | Description | Default | Options |
|
||||
| ----------------------------------- | ------------------------------------------------- | ------------------- | ------------------------------------------------------------------ |
|
||||
| `MODEL_NAME` | Path of the model weights | "facebook/opt-125m" | Local folder or Hugging Face repo ID |
|
||||
| `HF_TOKEN` | HuggingFace access token for gated/private models | | Your HuggingFace access token |
|
||||
| `MAX_MODEL_LEN` | Model's maximum context length | | Integer (e.g., 4096) |
|
||||
| `QUANTIZATION` | Quantization method | | "awq", "gptq", "squeezellm", "bitsandbytes" |
|
||||
| `TENSOR_PARALLEL_SIZE` | Number of GPUs | 1 | Integer |
|
||||
| `GPU_MEMORY_UTILIZATION` | Fraction of GPU memory to use | 0.95 | Float between 0.0 and 1.0 |
|
||||
| `MAX_NUM_SEQS` | Maximum number of sequences per iteration | 256 | Integer |
|
||||
| `CUSTOM_CHAT_TEMPLATE` | Custom chat template override | | Jinja2 template string |
|
||||
| `ENABLE_AUTO_TOOL_CHOICE` | Enable automatic tool selection | false | boolean (true or false) |
|
||||
| `TOOL_CALL_PARSER` | Parser for tool calls | | "mistral", "hermes", "llama3_json", "granite", "deepseek_v3", etc. |
|
||||
| `REASONING_PARSER` | Parser for reasoning-capable models | | "deepseek_r1", "qwen3", "granite", "hunyuan_a13b" |
|
||||
| `OPENAI_SERVED_MODEL_NAME_OVERRIDE` | Override served model name in API | | String |
|
||||
| `MAX_CONCURRENCY` | Maximum concurrent requests | 300 | Integer |
|
||||
|
||||
For complete configuration options, see the [full configuration documentation](https://github.com/runpod-workers/worker-vllm/blob/main/docs/configuration.md).
|
||||
|
||||
## API Usage
|
||||
|
||||
This worker supports two API formats: **RunPod native** and **OpenAI-compatible**.
|
||||
|
||||
### RunPod Native API
|
||||
|
||||
For testing directly in the RunPod UI, use these examples in your endpoint's request tab.
|
||||
|
||||
#### Chat Completions
|
||||
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"messages": [
|
||||
{ "role": "system", "content": "You are a helpful assistant." },
|
||||
{ "role": "user", "content": "What is the capital of France?" }
|
||||
],
|
||||
"sampling_params": {
|
||||
"max_tokens": 100,
|
||||
"temperature": 0.7
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Chat Completions (Streaming)
|
||||
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"messages": [
|
||||
{ "role": "user", "content": "Write a short story about a robot." }
|
||||
],
|
||||
"sampling_params": {
|
||||
"max_tokens": 500,
|
||||
"temperature": 0.8
|
||||
},
|
||||
"stream": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Text Generation
|
||||
|
||||
For direct text generation without chat format:
|
||||
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"prompt": "The capital of France is",
|
||||
"sampling_params": {
|
||||
"max_tokens": 64,
|
||||
"temperature": 0.0
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### List Models
|
||||
|
||||
```json
|
||||
{
|
||||
"input": {
|
||||
"openai_route": "/v1/models"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### OpenAI-Compatible API
|
||||
|
||||
For external clients and SDKs, use the `/openai/v1` path prefix with your RunPod API key.
|
||||
|
||||
#### Chat Completions
|
||||
|
||||
**Path:** `/openai/v1/chat/completions`
|
||||
|
||||
```json
|
||||
{
|
||||
"model": "meta-llama/Llama-2-7b-chat-hf",
|
||||
"messages": [
|
||||
{ "role": "system", "content": "You are a helpful assistant." },
|
||||
{ "role": "user", "content": "What is the capital of France?" }
|
||||
],
|
||||
"max_tokens": 100,
|
||||
"temperature": 0.7
|
||||
}
|
||||
```
|
||||
|
||||
#### Chat Completions (Streaming)
|
||||
|
||||
```json
|
||||
{
|
||||
"model": "meta-llama/Llama-2-7b-chat-hf",
|
||||
"messages": [
|
||||
{ "role": "user", "content": "Write a short story about a robot." }
|
||||
],
|
||||
"max_tokens": 500,
|
||||
"temperature": 0.8,
|
||||
"stream": true
|
||||
}
|
||||
```
|
||||
|
||||
#### Text Completions
|
||||
|
||||
**Path:** `/openai/v1/completions`
|
||||
|
||||
```json
|
||||
{
|
||||
"model": "meta-llama/Llama-2-7b-chat-hf",
|
||||
"prompt": "The capital of France is",
|
||||
"max_tokens": 100,
|
||||
"temperature": 0.7
|
||||
}
|
||||
```
|
||||
|
||||
#### List Models
|
||||
|
||||
**Path:** `/openai/v1/models`
|
||||
|
||||
```json
|
||||
{}
|
||||
```
|
||||
|
||||
#### Response Format
|
||||
|
||||
Both APIs return the same response format:
|
||||
|
||||
```json
|
||||
{
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"message": { "role": "assistant", "content": "Paris." },
|
||||
"finish_reason": "stop"
|
||||
}
|
||||
],
|
||||
"usage": { "prompt_tokens": 9, "completion_tokens": 1, "total_tokens": 10 }
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Usage
|
||||
|
||||
Below are minimal `python` snippets so you can copy-paste to get started quickly.
|
||||
|
||||
> Replace `<ENDPOINT_ID>` with your endpoint ID and `<API_KEY>` with a [RunPod API key](https://docs.runpod.io/get-started/api-keys).
|
||||
|
||||
### OpenAI compatible API
|
||||
|
||||
Minimal Python example using the official `openai` SDK:
|
||||
|
||||
```python
|
||||
from openai import OpenAI
|
||||
import os
|
||||
|
||||
# Initialize the OpenAI Client with your RunPod API Key and Endpoint URL
|
||||
client = OpenAI(
|
||||
api_key=os.getenv("RUNPOD_API_KEY"),
|
||||
base_url=f"https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1",
|
||||
)
|
||||
```
|
||||
|
||||
`Chat Completions (Non-Streaming)`
|
||||
|
||||
```python
|
||||
response = client.chat.completions.create(
|
||||
model="meta-llama/Llama-2-7b-chat-hf",
|
||||
messages=[{"role": "user", "content": "Explain quantum computing in simple terms"}],
|
||||
temperature=0,
|
||||
max_tokens=100,
|
||||
)
|
||||
print(f"Response: {response.choices[0].message.content}")
|
||||
```
|
||||
|
||||
`Chat Completions (Streaming)`
|
||||
|
||||
```python
|
||||
response_stream = client.chat.completions.create(
|
||||
model="meta-llama/Llama-2-7b-chat-hf",
|
||||
messages=[{"role": "user", "content": "Explain quantum computing in simple terms"}],
|
||||
temperature=0,
|
||||
max_tokens=100,
|
||||
stream=True
|
||||
)
|
||||
for response in response_stream:
|
||||
print(response.choices[0].delta.content or "", end="", flush=True)
|
||||
```
|
||||
|
||||
### RunPod Native API
|
||||
|
||||
```python
|
||||
import requests
|
||||
|
||||
response = requests.post(
|
||||
"https://api.runpod.ai/v2/<ENDPOINT_ID>/run",
|
||||
headers={"Authorization": "Bearer <API_KEY>"},
|
||||
json={
|
||||
"input": {
|
||||
"messages": [
|
||||
{"role": "system", "content": "You are a helpful assistant."},
|
||||
{"role": "user", "content": "Explain quantum computing in simple terms"}
|
||||
],
|
||||
"sampling_params": {
|
||||
"temperature": 0.7,
|
||||
"max_tokens": 150
|
||||
}
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
result = response.json()
|
||||
print(result["output"])
|
||||
```
|
||||
|
||||
## Compatibility
|
||||
|
||||
For supported models, see the [vLLM supported models documentation](https://docs.vllm.ai/en/latest/models/supported_models.html).
|
||||
|
||||
Anything not recognized by worker-vllm is forwarded to vLLM's engine, so advanced options in the vLLM docs (guided generation, LoRA, speculative decoding, etc.) also work.
|
||||
|
||||
## Documentation
|
||||
|
||||
- **[🚀 Deployment Guide](https://docs.runpod.io/serverless/vllm/get-started)** - Step-by-step setup
|
||||
- **[📖 Configuration Reference](https://github.com/runpod-workers/worker-vllm/blob/main/docs/configuration.md)** - All environment variables
|
||||
- **[🏗️ Advanced Deployment](https://github.com/runpod-workers/worker-vllm/blob/main/docs/deployment.md)** - Custom builds and strategies
|
||||
- **[🔧 Development Guide](https://github.com/runpod-workers/worker-vllm/blob/main/docs/conventions.md)** - Architecture and patterns
|
||||
|
||||
+18
-11
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"title": "vLLM",
|
||||
"description": "Deploy OpenAI-Compatible Blazing-Fast LLM Endpoints powered by the vLLM Inference Engine on RunPod Serverless",
|
||||
"description": "Deploy OpenAI-Compatible Blazing-Fast LLM Endpoints powered by vLLM",
|
||||
"type": "serverless",
|
||||
"category": "language",
|
||||
"iconUrl": "https://registry.npmmirror.com/@lobehub/icons-static-png/latest/files/dark/vllm-color.png",
|
||||
@@ -38,16 +38,6 @@
|
||||
"required": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"key": "HF_TOKEN",
|
||||
"input": {
|
||||
"name": "Access Token",
|
||||
"type": "string",
|
||||
"description": "Hugging Face access token for gated & private models",
|
||||
"default": "",
|
||||
"required": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"key": "TOKENIZER",
|
||||
"input": {
|
||||
@@ -1023,6 +1013,23 @@
|
||||
"default": "",
|
||||
"advanced": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"key": "REASONING_PARSER",
|
||||
"input": {
|
||||
"name": "Reasoning Parser",
|
||||
"type": "string",
|
||||
"description": "Parser for reasoning-capable models (enables reasoning mode)",
|
||||
"options": [
|
||||
{ "label": "None", "value": "" },
|
||||
{ "label": "DeepSeek R1", "value": "deepseek_r1" },
|
||||
{ "label": "Qwen3", "value": "qwen3" },
|
||||
{ "label": "Granite", "value": "granite" },
|
||||
{ "label": "Hunyuan A13B", "value": "hunyuan_a13b" }
|
||||
],
|
||||
"default": "",
|
||||
"advanced": true
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@@ -113,6 +113,7 @@ The way this works is that the first request will have a batch size of `DEFAULT_
|
||||
| `OPENAI_RESPONSE_ROLE` | `assistant` | `str` | Role of the LLM's Response in OpenAI Chat Completions. |
|
||||
| `ENABLE_AUTO_TOOL_CHOICE` | `false` | `bool` | Enables automatic tool selection for supported models. Set to `true` to activate. |
|
||||
| `TOOL_CALL_PARSER` | `None` | `str` | Specifies the parser for tool calls. Options: `mistral`, `hermes`, `llama3_json`, `llama4_json`, `llama4_pythonic`, `granite`, `granite-20b-fc`, `deepseek_v3`, `internlm`, `jamba`, `phi4_mini_json`, `pythonic` |
|
||||
| `REASONING_PARSER` | `None` | `str` | Parser for reasoning-capable models (enables reasoning mode). Examples: `deepseek_r1`, `qwen3`, `granite`, `hunyuan_a13b`. Leave unset to disable. |
|
||||
|
||||
## Serverless & Concurrency Settings
|
||||
|
||||
|
||||
Reference in New Issue
Block a user