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Author SHA1 Message Date
Marut PandyaandGitHub 7aa17463d3 Merge pull request #200 from runpod-workers/release/0.10.0
chore(release): 0.10.0
2025-08-11 15:33:51 -07:00
Marut PandyaandGitHub 72a643dd0c Merge pull request #207 from JhennerTigreros/main
Update requirements and engine creation to support new 0.10.0 vLLM version
2025-08-09 09:08:19 -07:00
Marut PandyaandGitHub 15f569f970 Merge pull request #208 from runpod-workers/revert-202-feat/proper-deployment
[Revert]"feat: added dev & release workflows; added conventions to support AI IDE"
2025-08-09 09:06:53 -07:00
Marut PandyaandGitHub 2f2bd4c749 Revert "feat: added dev & release workflows; added conventions to support AI IDE" 2025-08-09 09:01:52 -07:00
Jhenner Tigreros fb0c030797 fix initialization on openaiservingmodels 2025-08-07 16:40:06 -05:00
Jhenner Tigreros 8b02a703b4 fix issues 2025-08-07 15:59:43 -05:00
Jhenner Tigreros d8863139d6 add model to test 2025-08-07 15:28:02 -05:00
Jhenner TigrerosandGitHub f5a063956e Fix requirements.txt to support gpt-oss models 2025-08-07 15:13:37 -05:00
Marut PandyaandGitHub 18748fd73e Merge pull request #202 from runpod-workers/feat/proper-deployment
feat: added dev & release workflows; added conventions to support AI IDE
2025-08-04 17:13:47 -07:00
Tim Pietrusky 0133c23be8 ci: added manual workflow trigger for releases 2025-08-04 09:58:23 +02:00
Tim Pietrusky a129cff47d docs: use "version" instead of actual version, so that people can check the releases 2025-07-31 12:03:17 +02:00
Tim Pietrusky 30f2c4630e refactor: use correct version 2025-07-31 12:02:46 +02:00
Tim Pietrusky b98636e432 feat: added "dev" and "release" workflows; removed "vllm-base-image" as it's not needed 2025-07-28 16:40:20 +02:00
pandyamarut 185205c750 chore(release): 0.10.0
Signed-off-by: pandyamarut <pandyamarut@gmail.com>
2025-07-25 10:18:06 -07:00
pandyamarut b948e530a1 chore(release): 0.10.0
Signed-off-by: pandyamarut <pandyamarut@gmail.com>
2025-07-25 10:17:50 -07:00
Marut PandyaandGitHub 4f22d6f107 Merge pull request #193 from runpod-workers/release/0.9.1
chore(release): v0.9.1
2025-06-26 13:23:33 -07:00
pandyamarut 8839689132 chore(release): v0.9.1
Signed-off-by: pandyamarut <pandyamarut@gmail.com>
2025-06-26 11:06:23 -07:00
Marut PandyaandGitHub 5ddc2326cd Merge pull request #191 from runpod-workers/feat/0.9.1
feat: update to 0.9.1 & added CONFIG_FORMAT to run magistral
2025-06-26 10:17:54 -07:00
Tim Pietrusky 46a3300cbb chore: reverted changeds to only focus on vllm update 2025-06-20 14:56:39 +02:00
Tim Pietrusky 1e9a731380 Fix Mistral tokenizer initialization: let vLLM handle tokenizer for mistral models 2025-06-14 15:31:56 +02:00
Tim Pietrusky c47e649a24 Add CONFIG_FORMAT environment variable support 2025-06-14 15:12:46 +02:00
Tim Pietrusky 7192bcaeef Trigger build automatically on feat/0.9.1 branch 2025-06-14 13:49:22 +02:00
Tim Pietrusky 8665ffb78d Revert workflow back to original configuration 2025-06-14 13:47:16 +02:00
Tim Pietrusky 57431b30ad Fix workflow: use standard GitHub runners and actions 2025-06-14 13:42:46 +02:00
Tim Pietrusky 437a84c77a ci: added workflow to build the image 2025-06-14 13:31:36 +02:00
Tim Pietrusky a4062fc488 feat: update to 0.9.1 2025-06-14 13:31:27 +02:00
Marut PandyaandGitHub 9631407c1d Merge pull request #189 from runpod-workers/hf-mm
add multi modal env var
2025-06-10 17:03:18 -07:00
pandyamarut 1a93932ab2 add multi modal env var
Signed-off-by: pandyamarut <pandyamarut@gmail.com>
2025-06-10 17:02:42 -07:00
Marut PandyaandGitHub 4c6c88c3b9 Merge pull request #188 from WorstDev01/add-mm-limit-parameter
Add multimodal limit parameter support
2025-06-10 14:59:21 -07:00
WorstDev01 a58a76783d Add multimodal limit parameter support
- Updated convert_limit_mm_per_prompt to handle multiple types
- Added limit_mm_per_prompt parameter for image and video limits

Note: Consider adjusting default values - perhaps image limit > 1 or video=1
2025-06-10 23:36:47 +02:00
Marut PandyaandGitHub 26919c8849 Merge pull request #185 from runpod-workers/up-0.9.0
Version upgrade
2025-06-05 12:00:05 -07:00
pandyamarut 70cd1c8113 update vllm version 0.9.0
Signed-off-by: pandyamarut <pandyamarut@gmail.com>
2025-06-05 11:59:15 -07:00
Marut PandyaandGitHub deeff579f8 Merge pull request #183 from SorenDreano/fix/model_name_in_local_args
remove requirements for MODEL_NAME in local_model_args.json
2025-06-04 12:45:00 -07:00
Soren Dreano 11f96a09d7 remove requirements for MODEL_NAME in local_model_args.json
We want to use the same local_model_args.json for multiple models
which have different names. It would be very convenient to only
have a single local_args file and not have to create it every time

A warning should be enough for users
2025-05-23 17:59:19 +02:00
Marut PandyaandGitHub 23e8ecf85b Merge pull request #169 from RedHitMark/main
fix lora and multi-lora
2025-05-14 16:25:26 -07:00
Marut PandyaandGitHub 6db2c44d3b Update Dockerfile 2025-05-09 09:27:54 -07:00
Marut PandyaandGitHub 9b7ca4d0b0 Update tests.json 2025-05-08 17:12:59 -07:00
Marut PandyaandGitHub 2a4eaf0356 Merge pull request #182 from runpod-workers/up-0.8.5
update vllm
2025-05-08 11:40:23 -07:00
pandyamarut ba19cc97bf update vllm
Signed-off-by: pandyamarut <pandyamarut@gmail.com>
2025-05-08 11:34:58 -07:00
Marut PandyaandGitHub 6075f2c590 Merge pull request #181 from runpod-workers/revert-177-main
Revert "fix: added back limit_mm_per_prompt to engine args"
2025-05-07 12:31:15 -07:00
Marut PandyaandGitHub a9786a2481 Revert "fix: added back limit_mm_per_prompt to engine args" 2025-05-07 12:29:33 -07:00
Marut PandyaandGitHub 0b6bc7a2be Update tests.json 2025-05-07 11:14:50 -07:00
Marut PandyaandGitHub d0ab58ee17 Merge pull request #180 from muhsinking/patch-1
Update README.md table to fix table of contents links
2025-05-03 21:02:37 -07:00
Marut PandyaandGitHub 4e474c41c8 Merge pull request #177 from aleksandar-babic/main
fix: added back limit_mm_per_prompt to engine args
2025-05-03 21:01:58 -07:00
Mo KingandGitHub e9d13c155e Update README.md to fix table of contents links to environment variable sections
Separates environment variables into multiple tables, so that the table of contents will correctly jump to the appropriate section when clicked.
2025-04-30 12:45:54 -04:00
Marut PandyaandGitHub e807342e90 Merge pull request #178 from runpod-workers/up-0.8.4
update vllm
2025-04-21 13:15:03 -07:00
pandyamarut f33e8d2bcd update vllm
Signed-off-by: pandyamarut <pandyamarut@gmail.com>
2025-04-21 13:14:32 -07:00
Marut PandyaandGitHub 6b64bb93bd Merge pull request #147 from mohamednaji7/BitsAndBytes
completing the "bitsandbytes" option - based on  https://docs.vllm.ai/en/stable/quantization/bnb.html
2025-04-21 11:39:53 -07:00
Aleksandar Babic a53cf777ff chore: added limit_mm_per_prompt to worker config 2025-04-21 10:14:05 -04:00
Aleksandar Babic cfc258674b chore: added trailing comma to the final arg 2025-04-21 10:06:08 -04:00
Aleksandar Babic 8beafed06b fix: added back limit_mm_per_prompt to engine args 2025-04-21 10:02:46 -04:00
Marut PandyaandGitHub d77c53c3b7 Merge pull request #175 from runpod-workers/up-0.8.3
update vllm
2025-04-07 11:58:03 -07:00
pandyamarut 3d067cd472 update vllm
Signed-off-by: pandyamarut <pandyamarut@gmail.com>
2025-04-07 11:54:57 -07:00
Marut PandyaandGitHub f1360ccae7 Merge pull request #173 from KAJdev/patch-1
Update hub.json
2025-04-03 14:52:06 -07:00
Marut PandyaandGitHub 96f1b86126 Merge pull request #174 from KAJdev/patch-2
Update tests.json
2025-04-03 10:14:49 -07:00
Ezekiel WotringandGitHub 3a095f3e10 Update tests.json 2025-04-03 09:10:01 -08:00
Ezekiel WotringandGitHub 7cf7f3e4e6 Update hub.json 2025-04-03 09:04:28 -08:00
RedHitMark 084d000324 fix lora and multi-lora 2025-03-14 21:45:09 +01:00
Mohamed NagyandGitHub 04288240f6 updating the . in ['awq', 'squeezellm', 'gptq'. 'bitsandbytes'] for the QUNATIZATION row 2025-02-02 14:58:48 +02:00
Mohamed NagyandGitHub 9299f43b8b solving "typing_extensions" compatibility with "bitsandbytes"
```
2025-01-22 18:04:01 [INFO] > [stage-0 5/8] RUN --mount=type=cache,target=/root/.cache/pip python3 -m pip install --upgrade pip && python3 -m pip install --upgrade -r /requirements.txt:
2025-01-22 18:04:01 [INFO] #13 8.904
2025-01-22 18:04:01 [INFO] #13 8.904 The conflict is caused by:
2025-01-22 18:04:01 [INFO] #13 8.904 The user requested typing-extensions==4.7.1
2025-01-22 18:04:01 [INFO] #13 8.904 bitsandbytes 0.45.0 depends on typing_extensions>=4.8.0
```
2025-01-22 18:06:46 +02:00
mohamednaji7 131c17569f correct access to "args" dictionary 2025-01-21 22:33:28 +02:00
mohamednaji7 8882f6d50b adding 'bitsandbytes' to QUANTIZATION 2025-01-21 14:46:40 +02:00
mohamednaji7 331bc30101 adding 'bitsandbytes' option 2025-01-21 14:08:37 +02:00
mohamednaji7 a27f72a33a inforce args.quantization for bnb load_froamt 2025-01-21 14:01:32 +02:00
mohamednaji7 7167985f23 including bitsandbytes "src:https://docs.vllm.ai/en/stable/quantization/bnb.html" 2025-01-21 13:49:36 +02:00
10 changed files with 1605 additions and 1201 deletions
+997 -997
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@@ -1,44 +1,32 @@
{ {
"tests": [ "tests": [
{ {
"name": "basic_inference_test", "name": "basic_inference_test",
"input": { "input": {
"prompt": "Write a short poem about artificial intelligence.", "prompt": "Write a short poem about artificial intelligence."
}, },
"timeout": 30000 "timeout": 30000
} }
],
"config": {
"gpuTypeId": "NVIDIA GeForce RTX 4090",
"gpuCount": 1,
"env": [
{
"key": "MODEL_NAME",
"value": "facebook/opt-350m"
},
{
"key": "HF_TOKEN",
"value": "hf_dummy_token_for_testing_purposes_only"
},
{
"key": "MAX_MODEL_LEN",
"value": "8192"
},
{
"key": "GPU_MEMORY_UTILIZATION",
"value": "0.95"
}
], ],
"allowedCudaVersions": [ "config": {
"12.7", "gpuTypeId": "NVIDIA GeForce RTX 4090",
"12.6", "gpuCount": 1,
"12.5", "env": [
"12.4", {
"12.3", "key": "MODEL_NAME",
"12.2", "value": "facebook/opt-350m"
"12.1", }
"12.0", ],
"11.7" "allowedCudaVersions": [
] "12.7",
} "12.6",
"12.5",
"12.4",
"12.3",
"12.2",
"12.1",
"12.0",
"11.7"
]
}
} }
+2 -2
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@@ -12,7 +12,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install --upgrade -r /requirements.txt python3 -m pip install --upgrade -r /requirements.txt
# Install vLLM (switching back to pip installs since issues that required building fork are fixed and space optimization is not as important since caching) and FlashInfer # Install vLLM (switching back to pip installs since issues that required building fork are fixed and space optimization is not as important since caching) and FlashInfer
RUN python3 -m pip install vllm==0.8.2 && \ RUN python3 -m pip install vllm==0.10.0 && \
python3 -m pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3 python3 -m pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3
# Setup for Option 2: Building the Image with the Model included # Setup for Option 2: Building the Image with the Model included
@@ -47,4 +47,4 @@ RUN --mount=type=secret,id=HF_TOKEN,required=false \
fi fi
# Start the handler # Start the handler
CMD ["python3", "/src/handler.py"] CMD ["python3", "/src/handler.py"]
+39 -14
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@@ -18,9 +18,9 @@ Deploy OpenAI-Compatible Blazing-Fast LLM Endpoints powered by the [vLLM](https:
### 1. UI for Deploying vLLM Worker on RunPod console: ### 1. UI for Deploying vLLM Worker on RunPod console:
![Demo of Deploying vLLM Worker on RunPod console with new UI](media/ui_demo.gif) ![Demo of Deploying vLLM Worker on RunPod console with new UI](media/ui_demo.gif)
### 2. Worker vLLM `v2.2.0` with vLLM `0.8.2` now available under `stable` tags ### 2. Worker vLLM `v2.7.0` with vLLM `0.9.1` now available under `stable` tags
Update v2.2.0 is now available, use the image tag `runpod/worker-v1-vllm:v2.2.0stable-cuda12.1.0`. Update v2.7.0 is now available, use the image tag `runpod/worker-v1-vllm:v2.7.0stable-cuda12.1.0`.
### 3. OpenAI-Compatible [Embedding Worker](https://github.com/runpod-workers/worker-infinity-embedding) Released ### 3. OpenAI-Compatible [Embedding Worker](https://github.com/runpod-workers/worker-infinity-embedding) Released
Deploy your own OpenAI-compatible Serverless Endpoint on RunPod with multiple embedding models and fast inference for RAG and more! Deploy your own OpenAI-compatible Serverless Endpoint on RunPod with multiple embedding models and fast inference for RAG and more!
@@ -38,9 +38,8 @@ Worker vLLM is now cached on all RunPod machines, resulting in near-instant depl
- [Environment Variables](#environment-variables) - [Environment Variables](#environment-variables)
- [LLM Settings](#llm-settings) - [LLM Settings](#llm-settings)
- [Tokenizer Settings](#tokenizer-settings) - [Tokenizer Settings](#tokenizer-settings)
- [Tensor Parallelism (Multi-GPU) Settings](#tensor-parallelism-multi-gpu-settings) - [System and Parallelism Settings](#system-and-parallelism-settings)
- [System Settings](#system-settings) - [Streaming Batch Size Settings](#streaming-batch-size-settings)
- [Streaming Batch Size](#streaming-batch-size)
- [OpenAI Settings](#openai-settings) - [OpenAI Settings](#openai-settings)
- [Serverless Settings](#serverless-settings) - [Serverless Settings](#serverless-settings)
- [Option 2: Build Docker Image with Model Inside](#option-2-build-docker-image-with-model-inside) - [Option 2: Build Docker Image with Model Inside](#option-2-build-docker-image-with-model-inside)
@@ -82,7 +81,7 @@ Below is a summary of the available RunPod Worker images, categorized by image s
| CUDA Version | Stable Image Tag | Development Image Tag | Note | | CUDA Version | Stable Image Tag | Development Image Tag | Note |
|--------------|-----------------------------------|-----------------------------------|----------------------------------------------------------------------| |--------------|-----------------------------------|-----------------------------------|----------------------------------------------------------------------|
| 12.1.0 | `runpod/worker-v1-vllm:v2.2.0stable-cuda12.1.0` | `runpod/worker-v1-vllm:v2.2.0dev-cuda12.1.0` | When creating an Endpoint, select CUDA Version 12.3, 12.2 and 12.1 in the filter. | | 12.1.0 | `runpod/worker-v1-vllm:v2.7.0stable-cuda12.1.0` | `runpod/worker-v1-vllm:v2.7.0dev-cuda12.1.0` | When creating an Endpoint, select CUDA Version 12.3, 12.2 and 12.1 in the filter. |
@@ -91,9 +90,10 @@ Below is a summary of the available RunPod Worker images, categorized by image s
#### Prerequisites #### Prerequisites
- RunPod Account - RunPod Account
#### Environment Variables/Settings #### Environment Variables
> Note: `0` is equivalent to `False` and `1` is equivalent to `True` for boolean as int values. > Note: `0` is equivalent to `False` and `1` is equivalent to `True` for boolean as int values.
#### LLM Settings
| `Name` | `Default` | `Type/Choices` | `Description` | | `Name` | `Default` | `Type/Choices` | `Description` |
|-------------------------------------------|-----------------------|--------------------------------------------|---------------| |-------------------------------------------|-----------------------|--------------------------------------------|---------------|
| `MODEL_NAME` | 'facebook/opt-125m' | `str` | Name or path of the Hugging Face model to use. | | `MODEL_NAME` | 'facebook/opt-125m' | `str` | Name or path of the Hugging Face model to use. |
@@ -125,7 +125,7 @@ Below is a summary of the available RunPod Worker images, categorized by image s
| `MAX_NUM_SEQS` | 256 | `int` | Maximum number of sequences per iteration. | | `MAX_NUM_SEQS` | 256 | `int` | Maximum number of sequences per iteration. |
| `MAX_LOGPROBS` | 20 | `int` | Max number of log probs to return when logprobs is specified in SamplingParams. | | `MAX_LOGPROBS` | 20 | `int` | Max number of log probs to return when logprobs is specified in SamplingParams. |
| `DISABLE_LOG_STATS` | False | `bool` | Disable logging statistics. | | `DISABLE_LOG_STATS` | False | `bool` | Disable logging statistics. |
| `QUANTIZATION` | None | ['awq', 'squeezellm', 'gptq'] | Method used to quantize the weights. | | `QUANTIZATION` | None | ['awq', 'squeezellm', 'gptq', 'bitsandbytes'] | Method used to quantize the weights. |
| `ROPE_SCALING` | None | `dict` | RoPE scaling configuration in JSON format. | | `ROPE_SCALING` | None | `dict` | RoPE scaling configuration in JSON format. |
| `ROPE_THETA` | None | `float` | RoPE theta. Use with rope_scaling. | | `ROPE_THETA` | None | `float` | RoPE theta. Use with rope_scaling. |
| `TOKENIZER_POOL_SIZE` | 0 | `int` | Size of tokenizer pool to use for asynchronous tokenization. | | `TOKENIZER_POOL_SIZE` | 0 | `int` | Size of tokenizer pool to use for asynchronous tokenization. |
@@ -139,6 +139,7 @@ Below is a summary of the available RunPod Worker images, categorized by image s
| `LONG_LORA_SCALING_FACTORS` | None | `tuple` | Specify multiple scaling factors for LoRA adapters. | | `LONG_LORA_SCALING_FACTORS` | None | `tuple` | Specify multiple scaling factors for LoRA adapters. |
| `MAX_CPU_LORAS` | None | `int` | Maximum number of LoRAs to store in CPU memory. | | `MAX_CPU_LORAS` | None | `int` | Maximum number of LoRAs to store in CPU memory. |
| `FULLY_SHARDED_LORAS` | False | `bool` | Enable fully sharded LoRA layers. | | `FULLY_SHARDED_LORAS` | False | `bool` | Enable fully sharded LoRA layers. |
| `LORA_MODULES`| `[]`| `list[dict]`| Add lora adapters from Hugging Face `[{"name": "xx", "path": "xxx/xxxx", "base_model_name": "xxx/xxxx"}`|
| `SCHEDULER_DELAY_FACTOR` | 0.0 | `float` | Apply a delay before scheduling next prompt. | | `SCHEDULER_DELAY_FACTOR` | 0.0 | `float` | Apply a delay before scheduling next prompt. |
| `ENABLE_CHUNKED_PREFILL` | False | `bool` | Enable chunked prefill requests. | | `ENABLE_CHUNKED_PREFILL` | False | `bool` | Enable chunked prefill requests. |
| `SPECULATIVE_MODEL` | None | `str` | The name of the draft model to be used in speculative decoding. | | `SPECULATIVE_MODEL` | None | `str` | The name of the draft model to be used in speculative decoding. |
@@ -157,11 +158,20 @@ Below is a summary of the available RunPod Worker images, categorized by image s
| `PREEMPTION_CPU_CAPACITY` | 2 | `float` | The percentage of CPU memory used for the saved activations. | | `PREEMPTION_CPU_CAPACITY` | 2 | `float` | The percentage of CPU memory used for the saved activations. |
| `DISABLE_LOGGING_REQUEST` | False | `bool` | Disable logging requests. | | `DISABLE_LOGGING_REQUEST` | False | `bool` | Disable logging requests. |
| `MAX_LOG_LEN` | None | `int` | Max number of prompt characters or prompt ID numbers being printed in log. | | `MAX_LOG_LEN` | None | `int` | Max number of prompt characters or prompt ID numbers being printed in log. |
**Tokenizer Settings**
#### Tokenizer Settings
| `Name` | `Default` | `Type/Choices` | `Description` |
|-------------------------------------------|-----------------------|--------------------------------------------|---------------|
| `TOKENIZER_NAME` | `None` | `str` |Tokenizer repository to use a different tokenizer than the model's default. | | `TOKENIZER_NAME` | `None` | `str` |Tokenizer repository to use a different tokenizer than the model's default. |
| `TOKENIZER_REVISION` | `None` | `str` |Tokenizer revision to load. | | `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) | | `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**
#### System and Parallelism Settings
| `Name` | `Default` | `Type/Choices` | `Description` |
|-------------------------------------------|-----------------------|--------------------------------------------|---------------|
| `GPU_MEMORY_UTILIZATION` | `0.95` | `float` |Sets GPU VRAM utilization. | | `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. | | `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. | | `BLOCK_SIZE` | `16` | `8`, `16`, `32` |Token block size for contiguous chunks of tokens. |
@@ -169,16 +179,31 @@ Below is a summary of the available RunPod Worker images, categorized by image s
| `ENFORCE_EAGER` | False | `bool` |Always use eager-mode PyTorch. If False(`0`), will use eager mode and CUDA graph in hybrid for maximal performance and flexibility. | | `ENFORCE_EAGER` | False | `bool` |Always use eager-mode PyTorch. If False(`0`), will use eager mode and CUDA graph in hybrid for maximal performance and flexibility. |
| `MAX_SEQ_LEN_TO_CAPTURE` | `8192` | `int` |Maximum context length covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode.| | `MAX_SEQ_LEN_TO_CAPTURE` | `8192` | `int` |Maximum context length covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode.|
| `DISABLE_CUSTOM_ALL_REDUCE` | `0` | `int` |Enables or disables custom all reduce. | | `DISABLE_CUSTOM_ALL_REDUCE` | `0` | `int` |Enables or disables custom all reduce. |
**Streaming Batch Size Settings**:
#### Streaming Batch Size Settings
The way this works is that the first request will have a batch size of `DEFAULT_MIN_BATCH_SIZE`, and each subsequent request will have a batch size of `previous_batch_size * DEFAULT_BATCH_SIZE_GROWTH_FACTOR`. This will continue until the batch size reaches `DEFAULT_BATCH_SIZE`. E.g. for the default values, the batch sizes will be `1, 3, 9, 27, 50, 50, 50, ...`. You can also specify this per request, with inputs `max_batch_size`, `min_batch_size`, and `batch_size_growth_factor`. This has nothing to do with vLLM's internal batching, but rather the number of tokens sent in each HTTP request from the worker
| `Name` | `Default` | `Type/Choices` | `Description` |
|-------------------------------------------|-----------------------|--------------------------------------------|---------------|
| `DEFAULT_BATCH_SIZE` | `50` | `int` |Default and Maximum batch size for token streaming to reduce HTTP calls. | | `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_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. | | `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** #### OpenAI Settings
| `Name` | `Default` | `Type/Choices` | `Description` |
|-------------------------------------------|-----------------------|--------------------------------------------|---------------|
| `RAW_OPENAI_OUTPUT` | `1` | boolean as `int` |Enables raw OpenAI SSE format string output when streaming. **Required** to be enabled (which it is by default) for OpenAI compatibility. | | `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_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. | | `OPENAI_RESPONSE_ROLE` | `assistant` | `str` |Role of the LLM's Response in OpenAI Chat Completions. |
**Serverless Settings**
#### Serverless Settings
| `Name` | `Default` | `Type/Choices` | `Description` |
|-------------------------------------------|-----------------------|--------------------------------------------|---------------|
| `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 | | `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` | False | `bool` |Enables or disables vLLM stats logging. | | `DISABLE_LOG_STATS` | False | `bool` |Enables or disables vLLM stats logging. |
| `DISABLE_LOG_REQUESTS` | False | `bool` |Enables or disables vLLM request logging. | | `DISABLE_LOG_REQUESTS` | False | `bool` |Enables or disables vLLM request logging. |
+5 -2
View File
@@ -4,8 +4,11 @@ pyarrow
runpod~=1.7.7 runpod~=1.7.7
huggingface-hub huggingface-hub
packaging packaging
typing-extensions==4.7.1 typing-extensions>=4.8.0
pydantic pydantic
pydantic-settings pydantic-settings
hf-transfer hf-transfer
transformers transformers>=4.55.0
bitsandbytes>=0.45.0
kernels
torch==2.6.0
+2 -2
View File
@@ -7,7 +7,7 @@ variable "REPOSITORY" {
} }
variable "BASE_IMAGE_VERSION" { variable "BASE_IMAGE_VERSION" {
default = "v2.0.0stable" default = "v2.8.0stable"
} }
group "all" { group "all" {
@@ -29,4 +29,4 @@ target "worker-1210" {
WORKER_CUDA_VERSION = "12.1.0" WORKER_CUDA_VERSION = "12.1.0"
} }
output = ["type=docker,push=${PUSH}"] output = ["type=docker,push=${PUSH}"]
} }
+86 -18
View File
@@ -25,15 +25,68 @@ class vLLMEngine:
load_dotenv() # For local development load_dotenv() # For local development
self.engine_args = get_engine_args() self.engine_args = get_engine_args()
logging.info(f"Engine args: {self.engine_args}") logging.info(f"Engine args: {self.engine_args}")
self.tokenizer = TokenizerWrapper(self.engine_args.tokenizer or self.engine_args.model,
self.engine_args.tokenizer_revision, # Initialize vLLM engine first
self.engine_args.trust_remote_code)
self.llm = self._initialize_llm() if engine is None else engine.llm self.llm = self._initialize_llm() if engine is None else engine.llm
# Only create custom tokenizer wrapper if not using mistral tokenizer mode
# For mistral models, let vLLM handle tokenizer initialization
if self.engine_args.tokenizer_mode != 'mistral':
self.tokenizer = TokenizerWrapper(self.engine_args.tokenizer or self.engine_args.model,
self.engine_args.tokenizer_revision,
self.engine_args.trust_remote_code)
else:
# For mistral models, we'll get the tokenizer from vLLM later
self.tokenizer = None
self.max_concurrency = int(os.getenv("MAX_CONCURRENCY", DEFAULT_MAX_CONCURRENCY)) self.max_concurrency = int(os.getenv("MAX_CONCURRENCY", DEFAULT_MAX_CONCURRENCY))
self.default_batch_size = int(os.getenv("DEFAULT_BATCH_SIZE", DEFAULT_BATCH_SIZE)) 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.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)) self.min_batch_size = int(os.getenv("MIN_BATCH_SIZE", DEFAULT_MIN_BATCH_SIZE))
def _get_tokenizer_for_chat_template(self):
"""Get tokenizer for chat template application"""
if self.tokenizer is not None:
return self.tokenizer
else:
# For mistral models, get tokenizer from vLLM engine
# This is a fallback - ideally chat templates should be handled by vLLM directly
try:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
self.engine_args.tokenizer or self.engine_args.model,
revision=self.engine_args.tokenizer_revision or "main",
trust_remote_code=self.engine_args.trust_remote_code
)
# Create a minimal wrapper
class MinimalTokenizerWrapper:
def __init__(self, tokenizer):
self.tokenizer = tokenizer
self.custom_chat_template = os.getenv("CUSTOM_CHAT_TEMPLATE")
self.has_chat_template = bool(self.tokenizer.chat_template) or bool(self.custom_chat_template)
if self.custom_chat_template and isinstance(self.custom_chat_template, str):
self.tokenizer.chat_template = self.custom_chat_template
def apply_chat_template(self, input):
if isinstance(input, list):
if not self.has_chat_template:
raise ValueError(
"Chat template does not exist for this model, you must provide a single string input instead of a list of messages"
)
elif isinstance(input, str):
input = [{"role": "user", "content": input}]
else:
raise ValueError("Input must be a string or a list of messages")
return self.tokenizer.apply_chat_template(
input, tokenize=False, add_generation_prompt=True
)
return MinimalTokenizerWrapper(tokenizer)
except Exception as e:
logging.error(f"Failed to create fallback tokenizer: {e}")
raise e
def dynamic_batch_size(self, current_batch_size, batch_size_growth_factor): def dynamic_batch_size(self, current_batch_size, batch_size_growth_factor):
return min(current_batch_size*batch_size_growth_factor, self.default_batch_size) return min(current_batch_size*batch_size_growth_factor, self.default_batch_size)
@@ -55,7 +108,8 @@ class vLLMEngine:
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]: 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): if apply_chat_template or isinstance(llm_input, list):
llm_input = self.tokenizer.apply_chat_template(llm_input) tokenizer_wrapper = self._get_tokenizer_for_chat_template()
llm_input = tokenizer_wrapper.apply_chat_template(llm_input)
results_generator = self.llm.generate(llm_input, validated_sampling_params, request_id) 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 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} last_output_texts, token_counters = ["" for _ in range(n_responses)], {"batch": 0, "total": 0}
@@ -122,41 +176,55 @@ class OpenAIvLLMEngine(vLLMEngine):
super().__init__(vllm_engine) super().__init__(vllm_engine)
self.served_model_name = os.getenv("OPENAI_SERVED_MODEL_NAME_OVERRIDE") or self.engine_args.model self.served_model_name = os.getenv("OPENAI_SERVED_MODEL_NAME_OVERRIDE") or self.engine_args.model
self.response_role = os.getenv("OPENAI_RESPONSE_ROLE") or "assistant" self.response_role = os.getenv("OPENAI_RESPONSE_ROLE") or "assistant"
self.lora_adapters = self._load_lora_adapters()
asyncio.run(self._initialize_engines()) asyncio.run(self._initialize_engines())
self.raw_openai_output = bool(int(os.getenv("RAW_OPENAI_OUTPUT", 1))) self.raw_openai_output = bool(int(os.getenv("RAW_OPENAI_OUTPUT", 1)))
def _load_lora_adapters(self):
adapters = []
try:
adapters = json.loads(os.getenv("LORA_MODULES", '[]'))
except Exception as e:
logging.info(f"---Initialized adapter json load error: {e}")
for i, adapter in enumerate(adapters):
try:
adapters[i] = LoRAModulePath(**adapter)
logging.info(f"---Initialized adapter: {adapter}")
except Exception as e:
logging.info(f"---Initialized adapter not worked: {e}")
continue
return adapters
async def _initialize_engines(self): async def _initialize_engines(self):
self.model_config = await self.llm.get_model_config() self.model_config = await self.llm.get_model_config()
self.base_model_paths = [ self.base_model_paths = [
BaseModelPath(name=self.engine_args.model, model_path=self.engine_args.model) BaseModelPath(name=self.engine_args.model, model_path=self.engine_args.model)
] ]
lora_modules = os.getenv('LORA_MODULES', None)
if lora_modules is not None:
try:
lora_modules = json.loads(lora_modules)
lora_modules = [LoRAModulePath(**lora_modules)]
except:
lora_modules = None
self.serving_models = OpenAIServingModels( self.serving_models = OpenAIServingModels(
engine_client=self.llm, engine_client=self.llm,
model_config=self.model_config, model_config=self.model_config,
base_model_paths=self.base_model_paths, base_model_paths=self.base_model_paths,
lora_modules=None, lora_modules=self.lora_adapters,
prompt_adapters=None,
) )
await self.serving_models.init_static_loras()
# Get chat template from vLLM tokenizer if available
chat_template = None
if self.tokenizer and hasattr(self.tokenizer, 'tokenizer'):
chat_template = self.tokenizer.tokenizer.chat_template
self.chat_engine = OpenAIServingChat( self.chat_engine = OpenAIServingChat(
engine_client=self.llm, engine_client=self.llm,
model_config=self.model_config, model_config=self.model_config,
models=self.serving_models, models=self.serving_models,
response_role=self.response_role, response_role=self.response_role,
request_logger=None, request_logger=None,
chat_template=self.tokenizer.tokenizer.chat_template, chat_template=chat_template,
chat_template_content_format="auto", chat_template_content_format="auto",
# enable_reasoning=os.getenv('ENABLE_REASONING', 'false').lower() == 'true', # enable_reasoning=os.getenv('ENABLE_REASONING', 'false').lower() == 'true',
# reasoning_parser=None, reasoning_parser= os.getenv('REASONING_PARSER', "") or None,
# return_token_as_token_ids=False, # return_token_as_token_ids=False,
enable_auto_tools=os.getenv('ENABLE_AUTO_TOOL_CHOICE', 'false').lower() == 'true', enable_auto_tools=os.getenv('ENABLE_AUTO_TOOL_CHOICE', 'false').lower() == 'true',
tool_parser=os.getenv('TOOL_CALL_PARSER', "") or None, tool_parser=os.getenv('TOOL_CALL_PARSER', "") or None,
+8 -1
View File
@@ -26,6 +26,7 @@ DEFAULT_ARGS = {
"trust_remote_code": os.getenv('TRUST_REMOTE_CODE', 'False').lower() == 'true', "trust_remote_code": os.getenv('TRUST_REMOTE_CODE', 'False').lower() == 'true',
"download_dir": os.getenv('DOWNLOAD_DIR', None), "download_dir": os.getenv('DOWNLOAD_DIR', None),
"load_format": os.getenv('LOAD_FORMAT', 'auto'), "load_format": os.getenv('LOAD_FORMAT', 'auto'),
"config_format": os.getenv('CONFIG_FORMAT', 'auto'),
"dtype": os.getenv('DTYPE', 'auto'), "dtype": os.getenv('DTYPE', 'auto'),
"kv_cache_dtype": os.getenv('KV_CACHE_DTYPE', 'auto'), "kv_cache_dtype": os.getenv('KV_CACHE_DTYPE', 'auto'),
"quantization_param_path": os.getenv('QUANTIZATION_PARAM_PATH', None), "quantization_param_path": os.getenv('QUANTIZATION_PARAM_PATH', None),
@@ -92,6 +93,9 @@ DEFAULT_ARGS = {
"otlp_traces_endpoint": os.getenv('OTLP_TRACES_ENDPOINT', None), "otlp_traces_endpoint": os.getenv('OTLP_TRACES_ENDPOINT', None),
"use_v2_block_manager": os.getenv('USE_V2_BLOCK_MANAGER', 'true'), "use_v2_block_manager": os.getenv('USE_V2_BLOCK_MANAGER', 'true'),
} }
limit_mm_env = os.getenv('LIMIT_MM_PER_PROMPT')
if limit_mm_env is not None:
DEFAULT_ARGS["limit_mm_per_prompt"] = convert_limit_mm_per_prompt(limit_mm_env)
def match_vllm_args(args): def match_vllm_args(args):
"""Rename args to match vllm by: """Rename args to match vllm by:
@@ -122,7 +126,7 @@ def get_local_args():
local_args = json.load(f) local_args = json.load(f)
if local_args.get("MODEL_NAME") is None: if local_args.get("MODEL_NAME") is None:
raise ValueError("Model name not found in /local_model_args.json. There was a problem when baking the model in.") logging.warning("Model name not found in /local_model_args.json. There maybe was a problem when baking the model in.")
logging.info(f"Using baked in model with args: {local_args}") logging.info(f"Using baked in model with args: {local_args}")
os.environ["TRANSFORMERS_OFFLINE"] = "1" os.environ["TRANSFORMERS_OFFLINE"] = "1"
@@ -147,6 +151,9 @@ def get_engine_args():
# Rename and match to vllm args # Rename and match to vllm args
args = match_vllm_args(args) args = match_vllm_args(args)
if args.get("load_format") == "bitsandbytes":
args["quantization"] = args["load_format"]
# Set tensor parallel size and max parallel loading workers if more than 1 GPU is available # Set tensor parallel size and max parallel loading workers if more than 1 GPU is available
num_gpus = device_count() num_gpus = device_count()
+7 -2
View File
@@ -15,9 +15,14 @@ except ImportError:
logging.basicConfig(level=logging.INFO) logging.basicConfig(level=logging.INFO)
# Updated to parse multiple comma-separated multimodal limits (e.g., 'image=1,video=0')
def convert_limit_mm_per_prompt(input_string: str): def convert_limit_mm_per_prompt(input_string: str):
key, value = input_string.split('=') result = {}
return {key: int(value)} pairs = input_string.split(',')
for pair in pairs:
key, value = pair.split('=')
result[key] = int(value)
return result
def count_physical_cores(): def count_physical_cores():
with open('/proc/cpuinfo') as f: with open('/proc/cpuinfo') as f:
+430 -122
View File
@@ -1,5 +1,432 @@
{ {
"versions": { "versions": {
"0.10.0": {
"imageName": "runpod/worker-v1-vllm:v2.8.0stable-cuda12.1.0",
"minimumCudaVersion": "12.1",
"categories": [
{
"title": "LLM Settings",
"settings": [
"TOKENIZER", "TOKENIZER_MODE", "SKIP_TOKENIZER_INIT", "TRUST_REMOTE_CODE",
"DOWNLOAD_DIR", "LOAD_FORMAT", "DTYPE", "KV_CACHE_DTYPE", "QUANTIZATION_PARAM_PATH",
"MAX_MODEL_LEN", "GUIDED_DECODING_BACKEND", "DISTRIBUTED_EXECUTOR_BACKEND",
"WORKER_USE_RAY", "RAY_WORKERS_USE_NSIGHT", "PIPELINE_PARALLEL_SIZE",
"TENSOR_PARALLEL_SIZE", "MAX_PARALLEL_LOADING_WORKERS", "ENABLE_PREFIX_CACHING",
"DISABLE_SLIDING_WINDOW", "NUM_LOOKAHEAD_SLOTS",
"SEED", "NUM_GPU_BLOCKS_OVERRIDE", "MAX_NUM_BATCHED_TOKENS", "MAX_NUM_SEQS",
"MAX_LOGPROBS", "DISABLE_LOG_STATS", "QUANTIZATION", "ROPE_SCALING", "ROPE_THETA",
"TOKENIZER_POOL_SIZE", "TOKENIZER_POOL_TYPE", "TOKENIZER_POOL_EXTRA_CONFIG",
"ENABLE_LORA", "MAX_LORAS", "MAX_LORA_RANK", "LORA_EXTRA_VOCAB_SIZE",
"LORA_DTYPE", "LONG_LORA_SCALING_FACTORS", "MAX_CPU_LORAS", "FULLY_SHARDED_LORAS",
"DEVICE", "SCHEDULER_DELAY_FACTOR", "ENABLE_CHUNKED_PREFILL", "SPECULATIVE_MODEL",
"NUM_SPECULATIVE_TOKENS", "SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE",
"SPECULATIVE_MAX_MODEL_LEN", "SPECULATIVE_DISABLE_BY_BATCH_SIZE",
"NGRAM_PROMPT_LOOKUP_MAX", "NGRAM_PROMPT_LOOKUP_MIN", "SPEC_DECODING_ACCEPTANCE_METHOD",
"TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_THRESHOLD", "TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_ALPHA",
"MODEL_LOADER_EXTRA_CONFIG", "PREEMPTION_MODE", "PREEMPTION_CHECK_PERIOD",
"PREEMPTION_CPU_CAPACITY", "MAX_LOG_LEN", "DISABLE_LOGGING_REQUEST",
"ENABLE_AUTO_TOOL_CHOICE", "TOOL_CALL_PARSER"
]
},
{
"title": "Tokenizer Settings",
"settings": [
"TOKENIZER_NAME", "TOKENIZER_REVISION", "CUSTOM_CHAT_TEMPLATE"
]
},
{
"title": "System Settings",
"settings": [
"GPU_MEMORY_UTILIZATION", "MAX_PARALLEL_LOADING_WORKERS", "BLOCK_SIZE",
"SWAP_SPACE", "ENFORCE_EAGER", "MAX_SEQ_LEN_TO_CAPTURE", "DISABLE_CUSTOM_ALL_REDUCE"
]
},
{
"title": "Streaming Settings",
"settings": [
"DEFAULT_BATCH_SIZE", "DEFAULT_MIN_BATCH_SIZE", "DEFAULT_BATCH_SIZE_GROWTH_FACTOR"
]
},
{
"title": "OpenAI Settings",
"settings": [
"RAW_OPENAI_OUTPUT", "OPENAI_RESPONSE_ROLE", "OPENAI_SERVED_MODEL_NAME_OVERRIDE"
]
},
{
"title": "Serverless Settings",
"settings": [
"MAX_CONCURRENCY", "DISABLE_LOG_STATS", "DISABLE_LOG_REQUESTS"
]
}
]
},
"0.9.0": {
"imageName": "runpod/worker-v1-vllm:v2.6.0stable-cuda12.1.0",
"minimumCudaVersion": "12.1",
"categories": [
{
"title": "LLM Settings",
"settings": [
"TOKENIZER", "TOKENIZER_MODE", "SKIP_TOKENIZER_INIT", "TRUST_REMOTE_CODE",
"DOWNLOAD_DIR", "LOAD_FORMAT", "DTYPE", "KV_CACHE_DTYPE", "QUANTIZATION_PARAM_PATH",
"MAX_MODEL_LEN", "GUIDED_DECODING_BACKEND", "DISTRIBUTED_EXECUTOR_BACKEND",
"WORKER_USE_RAY", "RAY_WORKERS_USE_NSIGHT", "PIPELINE_PARALLEL_SIZE",
"TENSOR_PARALLEL_SIZE", "MAX_PARALLEL_LOADING_WORKERS", "ENABLE_PREFIX_CACHING",
"DISABLE_SLIDING_WINDOW", "NUM_LOOKAHEAD_SLOTS",
"SEED", "NUM_GPU_BLOCKS_OVERRIDE", "MAX_NUM_BATCHED_TOKENS", "MAX_NUM_SEQS",
"MAX_LOGPROBS", "DISABLE_LOG_STATS", "QUANTIZATION", "ROPE_SCALING", "ROPE_THETA",
"TOKENIZER_POOL_SIZE", "TOKENIZER_POOL_TYPE", "TOKENIZER_POOL_EXTRA_CONFIG",
"ENABLE_LORA", "MAX_LORAS", "MAX_LORA_RANK", "LORA_EXTRA_VOCAB_SIZE",
"LORA_DTYPE", "LONG_LORA_SCALING_FACTORS", "MAX_CPU_LORAS", "FULLY_SHARDED_LORAS",
"DEVICE", "SCHEDULER_DELAY_FACTOR", "ENABLE_CHUNKED_PREFILL", "SPECULATIVE_MODEL",
"NUM_SPECULATIVE_TOKENS", "SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE",
"SPECULATIVE_MAX_MODEL_LEN", "SPECULATIVE_DISABLE_BY_BATCH_SIZE",
"NGRAM_PROMPT_LOOKUP_MAX", "NGRAM_PROMPT_LOOKUP_MIN", "SPEC_DECODING_ACCEPTANCE_METHOD",
"TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_THRESHOLD", "TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_ALPHA",
"MODEL_LOADER_EXTRA_CONFIG", "PREEMPTION_MODE", "PREEMPTION_CHECK_PERIOD",
"PREEMPTION_CPU_CAPACITY", "MAX_LOG_LEN", "DISABLE_LOGGING_REQUEST",
"ENABLE_AUTO_TOOL_CHOICE", "TOOL_CALL_PARSER"
]
},
{
"title": "Tokenizer Settings",
"settings": [
"TOKENIZER_NAME", "TOKENIZER_REVISION", "CUSTOM_CHAT_TEMPLATE"
]
},
{
"title": "System Settings",
"settings": [
"GPU_MEMORY_UTILIZATION", "MAX_PARALLEL_LOADING_WORKERS", "BLOCK_SIZE",
"SWAP_SPACE", "ENFORCE_EAGER", "MAX_SEQ_LEN_TO_CAPTURE", "DISABLE_CUSTOM_ALL_REDUCE"
]
},
{
"title": "Streaming Settings",
"settings": [
"DEFAULT_BATCH_SIZE", "DEFAULT_MIN_BATCH_SIZE", "DEFAULT_BATCH_SIZE_GROWTH_FACTOR"
]
},
{
"title": "OpenAI Settings",
"settings": [
"RAW_OPENAI_OUTPUT", "OPENAI_RESPONSE_ROLE", "OPENAI_SERVED_MODEL_NAME_OVERRIDE"
]
},
{
"title": "Serverless Settings",
"settings": [
"MAX_CONCURRENCY", "DISABLE_LOG_STATS", "DISABLE_LOG_REQUESTS"
]
}
]
},
"0.9.1": {
"imageName": "runpod/worker-v1-vllm:v2.7.0stable-cuda12.1.0",
"minimumCudaVersion": "12.1",
"categories": [
{
"title": "LLM Settings",
"settings": [
"TOKENIZER", "TOKENIZER_MODE", "SKIP_TOKENIZER_INIT", "TRUST_REMOTE_CODE",
"DOWNLOAD_DIR", "LOAD_FORMAT", "DTYPE", "KV_CACHE_DTYPE", "QUANTIZATION_PARAM_PATH",
"MAX_MODEL_LEN", "GUIDED_DECODING_BACKEND", "DISTRIBUTED_EXECUTOR_BACKEND",
"WORKER_USE_RAY", "RAY_WORKERS_USE_NSIGHT", "PIPELINE_PARALLEL_SIZE",
"TENSOR_PARALLEL_SIZE", "MAX_PARALLEL_LOADING_WORKERS", "ENABLE_PREFIX_CACHING",
"DISABLE_SLIDING_WINDOW", "NUM_LOOKAHEAD_SLOTS",
"SEED", "NUM_GPU_BLOCKS_OVERRIDE", "MAX_NUM_BATCHED_TOKENS", "MAX_NUM_SEQS",
"MAX_LOGPROBS", "DISABLE_LOG_STATS", "QUANTIZATION", "ROPE_SCALING", "ROPE_THETA",
"TOKENIZER_POOL_SIZE", "TOKENIZER_POOL_TYPE", "TOKENIZER_POOL_EXTRA_CONFIG",
"ENABLE_LORA", "MAX_LORAS", "MAX_LORA_RANK", "LORA_EXTRA_VOCAB_SIZE",
"LORA_DTYPE", "LONG_LORA_SCALING_FACTORS", "MAX_CPU_LORAS", "FULLY_SHARDED_LORAS",
"DEVICE", "SCHEDULER_DELAY_FACTOR", "ENABLE_CHUNKED_PREFILL", "SPECULATIVE_MODEL",
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@@ -365,126 +792,6 @@
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"schema": { "schema": {
@@ -741,14 +1048,15 @@
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"value": "", "value": "",
"title": "Quantization", "title": "Quantization",
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"type": "select", "type": "select",
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{ "value": "awq", "label": "AWQ" }, { "value": "awq", "label": "AWQ" },
{ "value": "squeezellm", "label": "SqueezeLLM" }, { "value": "squeezellm", "label": "SqueezeLLM" },
{ "value": "gptq", "label": "GPTQ" } { "value": "gptq", "label": "GPTQ" },
{ "value": "bitsandbytes", "label": "bitsandbytes" }
] ]
}, },
"ROPE_SCALING": { "ROPE_SCALING": {