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docs/source/features/quantization/supported_hardware.md deleted 100644 → 0
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(quantization-supported-hardware)=
# Supported Hardware
The table below shows the compatibility of various quantization implementations with different hardware platforms in vLLM:
:::{list-table}
:header-rows: 1
:widths: 20 8 8 8 8 8 8 8 8 8 8
- * Implementation
* Volta
* Turing
* Ampere
* Ada
* Hopper
* AMD GPU
* Intel GPU
* x86 CPU
* AWS Inferentia
* Google TPU
- * AWQ
* ❌
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ❌
* ✅︎
* ✅︎
* ❌
* ❌
- * GPTQ
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ❌
* ✅︎
* ✅︎
* ❌
* ❌
- * Marlin (GPTQ/AWQ/FP8)
* ❌
* ❌
* ✅︎
* ✅︎
* ✅︎
* ❌
* ❌
* ❌
* ❌
* ❌
- * INT8 (W8A8)
* ❌
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ❌
* ❌
* ✅︎
* ❌
* ✅︎
- * FP8 (W8A8)
* ❌
* ❌
* ❌
* ✅︎
* ✅︎
* ✅︎
* ❌
* ❌
* ❌
* ❌
- * BitBLAS (GPTQ)
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ❌
* ❌
* ❌
* ❌
* ❌
- * AQLM
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ❌
* ❌
* ❌
* ❌
* ❌
- * bitsandbytes
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ❌
* ❌
* ❌
* ❌
* ❌
- * DeepSpeedFP
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ❌
* ❌
* ❌
* ❌
* ❌
- * GGUF
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ❌
* ❌
* ❌
* ❌
- * modelopt
* ✅︎
* ✅︎
* ✅︎
* ✅︎
* ✅︎︎
* ❌
* ❌
* ❌
* ❌
* ❌
:::
- Volta refers to SM 7.0, Turing to SM 7.5, Ampere to SM 8.0/8.6, Ada to SM 8.9, and Hopper to SM 9.0.
- ✅︎ indicates that the quantization method is supported on the specified hardware.
- ❌ indicates that the quantization method is not supported on the specified hardware.
:::{note}
This compatibility chart is subject to change as vLLM continues to evolve and expand its support for different hardware platforms and quantization methods.
For the most up-to-date information on hardware support and quantization methods, please refer to <gh-dir:vllm/model_executor/layers/quantization> or consult with the vLLM development team.
:::
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(installation-index)=
# Installation
vLLM supports the following hardware platforms:
:::{toctree}
:maxdepth: 1
:hidden:
installation/gpu
installation/cpu
installation/ai_accelerator
:::
- <project:installation/gpu.md>
- NVIDIA CUDA
- AMD ROCm
- Intel XPU
- <project:installation/cpu.md>
- Intel/AMD x86
- ARM AArch64
- Apple silicon
- IBM Z (S390X)
- <project:installation/ai_accelerator.md>
- Google TPU
- Intel Gaudi
- AWS Neuron
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# Other AI accelerators
vLLM is a Python library that supports the following AI accelerators. Select your AI accelerator type to see vendor specific instructions:
:::::{tab-set}
:sync-group: device
::::{tab-item} Google TPU
:selected:
:sync: tpu
:::{include} ai_accelerator/tpu.inc.md
:start-after: "# Installation"
:end-before: "## Requirements"
:::
::::
::::{tab-item} Intel Gaudi
:sync: hpu-gaudi
:::{include} ai_accelerator/hpu-gaudi.inc.md
:start-after: "# Installation"
:end-before: "## Requirements"
:::
::::
::::{tab-item} AWS Neuron
:sync: neuron
:::{include} ai_accelerator/neuron.inc.md
:start-after: "# Installation"
:end-before: "## Requirements"
:::
::::
:::::
## Requirements
:::::{tab-set}
:sync-group: device
::::{tab-item} Google TPU
:sync: tpu
:::{include} ai_accelerator/tpu.inc.md
:start-after: "## Requirements"
:end-before: "## Configure a new environment"
:::
::::
::::{tab-item} Intel Gaudi
:sync: hpu-gaudi
:::{include} ai_accelerator/hpu-gaudi.inc.md
:start-after: "## Requirements"
:end-before: "## Configure a new environment"
:::
::::
::::{tab-item} AWS Neuron
:sync: neuron
:::{include} ai_accelerator/neuron.inc.md
:start-after: "## Requirements"
:end-before: "## Configure a new environment"
:::
::::
:::::
## Configure a new environment
:::::{tab-set}
:sync-group: device
::::{tab-item} Google TPU
:sync: tpu
:::{include} ai_accelerator/tpu.inc.md
:start-after: "## Configure a new environment"
:end-before: "## Set up using Python"
:::
::::
::::{tab-item} Intel Gaudi
:sync: hpu-gaudi
:::{include} ai_accelerator/hpu-gaudi.inc.md
:start-after: "## Configure a new environment"
:end-before: "## Set up using Python"
:::
::::
::::{tab-item} AWS Neuron
:sync: neuron
:::{include} ai_accelerator/neuron.inc.md
:start-after: "## Configure a new environment"
:end-before: "## Set up using Python"
:::
::::
:::::
## Set up using Python
### Pre-built wheels
:::::{tab-set}
:sync-group: device
::::{tab-item} Google TPU
:sync: tpu
:::{include} ai_accelerator/tpu.inc.md
:start-after: "### Pre-built wheels"
:end-before: "### Build wheel from source"
:::
::::
::::{tab-item} Intel Gaudi
:sync: hpu-gaudi
:::{include} ai_accelerator/hpu-gaudi.inc.md
:start-after: "### Pre-built wheels"
:end-before: "### Build wheel from source"
:::
::::
::::{tab-item} AWS Neuron
:sync: neuron
:::{include} ai_accelerator/neuron.inc.md
:start-after: "### Pre-built wheels"
:end-before: "### Build wheel from source"
:::
::::
:::::
### Build wheel from source
:::::{tab-set}
:sync-group: device
::::{tab-item} Google TPU
:sync: tpu
:::{include} ai_accelerator/tpu.inc.md
:start-after: "### Build wheel from source"
:end-before: "## Set up using Docker"
:::
::::
::::{tab-item} Intel Gaudi
:sync: hpu-gaudi
:::{include} ai_accelerator/hpu-gaudi.inc.md
:start-after: "### Build wheel from source"
:end-before: "## Set up using Docker"
:::
::::
::::{tab-item} AWS Neuron
:sync: neuron
:::{include} ai_accelerator/neuron.inc.md
:start-after: "### Build wheel from source"
:end-before: "## Set up using Docker"
:::
::::
:::::
## Set up using Docker
### Pre-built images
:::::{tab-set}
:sync-group: device
::::{tab-item} Google TPU
:sync: tpu
:::{include} ai_accelerator/tpu.inc.md
:start-after: "### Pre-built images"
:end-before: "### Build image from source"
:::
::::
::::{tab-item} Intel Gaudi
:sync: hpu-gaudi
:::{include} ai_accelerator/hpu-gaudi.inc.md
:start-after: "### Pre-built images"
:end-before: "### Build image from source"
:::
::::
::::{tab-item} AWS Neuron
:sync: neuron
:::{include} ai_accelerator/neuron.inc.md
:start-after: "### Pre-built images"
:end-before: "### Build image from source"
:::
::::
:::::
### Build image from source
:::::{tab-set}
:sync-group: device
::::{tab-item} Google TPU
:sync: tpu
:::{include} ai_accelerator/tpu.inc.md
:start-after: "### Build image from source"
:end-before: "## Extra information"
:::
::::
::::{tab-item} Intel Gaudi
:sync: hpu-gaudi
:::{include} ai_accelerator/hpu-gaudi.inc.md
:start-after: "### Build image from source"
:end-before: "## Extra information"
:::
::::
::::{tab-item} AWS Neuron
:sync: neuron
:::{include} ai_accelerator/neuron.inc.md
:start-after: "### Build image from source"
:end-before: "## Extra information"
:::
::::
:::::
## Extra information
:::::{tab-set}
:sync-group: device
::::{tab-item} Google TPU
:sync: tpu
:::{include} ai_accelerator/tpu.inc.md
:start-after: "## Extra information"
:::
::::
::::{tab-item} Intel Gaudi
:sync: hpu-gaudi
:::{include} ai_accelerator/hpu-gaudi.inc.md
:start-after: "## Extra information"
:::
::::
::::{tab-item} AWS Neuron
:sync: neuron
:::{include} ai_accelerator/neuron.inc.md
:start-after: "## Extra information"
:::
::::
:::::
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# Installation
vLLM initially supports basic model inferencing and serving on x86 CPU platform, with data types FP32, FP16 and BF16.
:::{attention}
There are no pre-built wheels or images for this device, so you must build vLLM from source.
:::
## Requirements
- OS: Linux
- Compiler: `gcc/g++ >= 12.3.0` (optional, recommended)
- Instruction Set Architecture (ISA): AVX512 (optional, recommended)
:::{tip}
[Intel Extension for PyTorch (IPEX)](https://github.com/intel/intel-extension-for-pytorch) extends PyTorch with up-to-date features optimizations for an extra performance boost on Intel hardware.
:::
## Set up using Python
### Pre-built wheels
### Build wheel from source
:::{include} cpu/build.inc.md
:::
:::{note}
- AVX512_BF16 is an extension ISA provides native BF16 data type conversion and vector product instructions, which brings some performance improvement compared with pure AVX512. The CPU backend build script will check the host CPU flags to determine whether to enable AVX512_BF16.
- If you want to force enable AVX512_BF16 for the cross-compilation, please set environment variable `VLLM_CPU_AVX512BF16=1` before the building.
:::
## Set up using Docker
### Pre-built images
See [https://gallery.ecr.aws/q9t5s3a7/vllm-cpu-release-repo](https://gallery.ecr.aws/q9t5s3a7/vllm-cpu-release-repo)
### Build image from source
## Extra information
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# GPU
vLLM is a Python library that supports the following GPU variants. Select your GPU type to see vendor specific instructions:
:::::{tab-set}
:sync-group: device
::::{tab-item} NVIDIA CUDA
:selected:
:sync: cuda
:::{include} gpu/cuda.inc.md
:start-after: "# Installation"
:end-before: "## Requirements"
:::
::::
::::{tab-item} AMD ROCm
:sync: rocm
:::{include} gpu/rocm.inc.md
:start-after: "# Installation"
:end-before: "## Requirements"
:::
::::
::::{tab-item} Intel XPU
:sync: xpu
:::{include} gpu/xpu.inc.md
:start-after: "# Installation"
:end-before: "## Requirements"
:::
::::
:::::
## Requirements
- OS: Linux
- Python: 3.9 -- 3.12
:::::{tab-set}
:sync-group: device
::::{tab-item} NVIDIA CUDA
:sync: cuda
:::{include} gpu/cuda.inc.md
:start-after: "## Requirements"
:end-before: "## Set up using Python"
:::
::::
::::{tab-item} AMD ROCm
:sync: rocm
:::{include} gpu/rocm.inc.md
:start-after: "## Requirements"
:end-before: "## Set up using Python"
:::
::::
::::{tab-item} Intel XPU
:sync: xpu
:::{include} gpu/xpu.inc.md
:start-after: "## Requirements"
:end-before: "## Set up using Python"
:::
::::
:::::
## Set up using Python
### Create a new Python environment
:::{include} python_env_setup.inc.md
:::
:::::{tab-set}
:sync-group: device
::::{tab-item} NVIDIA CUDA
:sync: cuda
:::{include} gpu/cuda.inc.md
:start-after: "## Create a new Python environment"
:end-before: "### Pre-built wheels"
:::
::::
::::{tab-item} AMD ROCm
:sync: rocm
There is no extra information on creating a new Python environment for this device.
::::
::::{tab-item} Intel XPU
:sync: xpu
There is no extra information on creating a new Python environment for this device.
::::
:::::
### Pre-built wheels
:::::{tab-set}
:sync-group: device
::::{tab-item} NVIDIA CUDA
:sync: cuda
:::{include} gpu/cuda.inc.md
:start-after: "### Pre-built wheels"
:end-before: "### Build wheel from source"
:::
::::
::::{tab-item} AMD ROCm
:sync: rocm
:::{include} gpu/rocm.inc.md
:start-after: "### Pre-built wheels"
:end-before: "### Build wheel from source"
:::
::::
::::{tab-item} Intel XPU
:sync: xpu
:::{include} gpu/xpu.inc.md
:start-after: "### Pre-built wheels"
:end-before: "### Build wheel from source"
:::
::::
:::::
(build-from-source)=
### Build wheel from source
:::::{tab-set}
:sync-group: device
::::{tab-item} NVIDIA CUDA
:sync: cuda
:::{include} gpu/cuda.inc.md
:start-after: "### Build wheel from source"
:end-before: "## Set up using Docker"
:::
::::
::::{tab-item} AMD ROCm
:sync: rocm
:::{include} gpu/rocm.inc.md
:start-after: "### Build wheel from source"
:end-before: "## Set up using Docker"
:::
::::
::::{tab-item} Intel XPU
:sync: xpu
:::{include} gpu/xpu.inc.md
:start-after: "### Build wheel from source"
:end-before: "## Set up using Docker"
:::
::::
:::::
## Set up using Docker
### Pre-built images
:::::{tab-set}
:sync-group: device
::::{tab-item} NVIDIA CUDA
:sync: cuda
:::{include} gpu/cuda.inc.md
:start-after: "### Pre-built images"
:end-before: "### Build image from source"
:::
::::
::::{tab-item} AMD ROCm
:sync: rocm
:::{include} gpu/rocm.inc.md
:start-after: "### Pre-built images"
:end-before: "### Build image from source"
:::
::::
::::{tab-item} Intel XPU
:sync: xpu
:::{include} gpu/xpu.inc.md
:start-after: "### Pre-built images"
:end-before: "### Build image from source"
:::
::::
:::::
### Build image from source
:::::{tab-set}
:sync-group: device
::::{tab-item} NVIDIA CUDA
:sync: cuda
:::{include} gpu/cuda.inc.md
:start-after: "### Build image from source"
:end-before: "## Supported features"
:::
::::
::::{tab-item} AMD ROCm
:sync: rocm
:::{include} gpu/rocm.inc.md
:start-after: "### Build image from source"
:end-before: "## Supported features"
:::
::::
::::{tab-item} Intel XPU
:sync: xpu
:::{include} gpu/xpu.inc.md
:start-after: "### Build image from source"
:end-before: "## Supported features"
:::
::::
:::::
## Supported features
:::::{tab-set}
:sync-group: device
::::{tab-item} NVIDIA CUDA
:sync: cuda
:::{include} gpu/cuda.inc.md
:start-after: "## Supported features"
:::
::::
::::{tab-item} AMD ROCm
:sync: rocm
:::{include} gpu/rocm.inc.md
:start-after: "## Supported features"
:::
::::
::::{tab-item} Intel XPU
:sync: xpu
:::{include} gpu/xpu.inc.md
:start-after: "## Supported features"
:::
::::
:::::
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# Welcome to vLLM
:::{figure} ./assets/logos/vllm-logo-text-light.png
:align: center
:alt: vLLM
:class: no-scaled-link
:width: 60%
:::
:::{raw} html
<p style="text-align:center">
<strong>Easy, fast, and cheap LLM serving for everyone
</strong>
</p>
<p style="text-align:center">
<script async defer src="https://buttons.github.io/buttons.js"></script>
<a class="github-button" href="https://github.com/vllm-project/vllm" data-show-count="true" data-size="large" aria-label="Star">Star</a>
<a class="github-button" href="https://github.com/vllm-project/vllm/subscription" data-icon="octicon-eye" data-size="large" aria-label="Watch">Watch</a>
<a class="github-button" href="https://github.com/vllm-project/vllm/fork" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork">Fork</a>
</p>
:::
vLLM is a fast and easy-to-use library for LLM inference and serving.
Originally developed in the [Sky Computing Lab](https://sky.cs.berkeley.edu) at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.
vLLM is fast with:
- State-of-the-art serving throughput
- Efficient management of attention key and value memory with [**PagedAttention**](https://blog.vllm.ai/2023/06/20/vllm.html)
- Continuous batching of incoming requests
- Fast model execution with CUDA/HIP graph
- Quantization: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), INT4, INT8, and FP8
- Optimized CUDA kernels, including integration with FlashAttention and FlashInfer.
- Speculative decoding
- Chunked prefill
vLLM is flexible and easy to use with:
- Seamless integration with popular HuggingFace models
- High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
- Tensor parallelism and pipeline parallelism support for distributed inference
- Streaming outputs
- OpenAI-compatible API server
- Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs, Gaudi® accelerators and GPUs, IBM Power CPUs, TPU, and AWS Trainium and Inferentia Accelerators.
- Prefix caching support
- Multi-lora support
For more information, check out the following:
- [vLLM announcing blog post](https://vllm.ai) (intro to PagedAttention)
- [vLLM paper](https://arxiv.org/abs/2309.06180) (SOSP 2023)
- [How continuous batching enables 23x throughput in LLM inference while reducing p50 latency](https://www.anyscale.com/blog/continuous-batching-llm-inference) by Cade Daniel et al.
- [vLLM Meetups](#meetups)
## Documentation
% How to start using vLLM?
:::{toctree}
:caption: Getting Started
:maxdepth: 1
getting_started/installation
getting_started/quickstart
getting_started/examples/examples_index
getting_started/troubleshooting
getting_started/faq
getting_started/v1_user_guide
:::
% What does vLLM support?
:::{toctree}
:caption: Models
:maxdepth: 1
models/supported_models
models/generative_models
models/pooling_models
models/extensions/index
:::
% Additional capabilities
:::{toctree}
:caption: Features
:maxdepth: 1
features/quantization/index
features/multimodal_inputs
features/prompt_embeds
features/lora
features/tool_calling
features/reasoning_outputs
features/structured_outputs
features/automatic_prefix_caching
features/disagg_prefill
features/spec_decode
features/compatibility_matrix
:::
% Details about running vLLM
:::{toctree}
:caption: Training
:maxdepth: 1
training/trl.md
training/rlhf.md
:::
:::{toctree}
:caption: Inference and Serving
:maxdepth: 1
serving/offline_inference
serving/openai_compatible_server
serving/serve_args
serving/distributed_serving
serving/metrics
serving/engine_args
serving/env_vars
serving/usage_stats
serving/integrations/index
:::
% Scaling up vLLM for production
:::{toctree}
:caption: Deployment
:maxdepth: 1
deployment/security
deployment/docker
deployment/k8s
deployment/nginx
deployment/frameworks/index
deployment/integrations/index
:::
% Making the most out of vLLM
:::{toctree}
:caption: Performance
:maxdepth: 1
performance/optimization
performance/benchmarks
:::
% Explanation of vLLM internals
:::{toctree}
:caption: Design Documents
:maxdepth: 2
design/arch_overview
design/huggingface_integration
design/plugin_system
design/kernel/paged_attention
design/mm_processing
design/automatic_prefix_caching
design/multiprocessing
:::
:::{toctree}
:caption: V1 Design Documents
:maxdepth: 2
design/v1/torch_compile
design/v1/prefix_caching
design/v1/metrics
:::
% How to contribute to the vLLM project
:::{toctree}
:caption: Developer Guide
:maxdepth: 2
contributing/overview
contributing/deprecation_policy
contributing/profiling/profiling_index
contributing/dockerfile/dockerfile
contributing/model/index
contributing/vulnerability_management
:::
% Technical API specifications
:::{toctree}
:caption: API Reference
:maxdepth: 2
api/summary
api/vllm/vllm
:::
% Latest news and acknowledgements
:::{toctree}
:caption: Community
:maxdepth: 1
community/blog
community/meetups
community/sponsors
:::
## Indices and tables
- {ref}`genindex`
- {ref}`modindex`
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# Built-in Extensions
:::{toctree}
:maxdepth: 1
runai_model_streamer
tensorizer
fastsafetensor
:::
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(supported-models)=
# Supported Models
vLLM supports [generative](generative-models) and [pooling](pooling-models) models across various tasks.
If a model supports more than one task, you can set the task via the `--task` argument.
For each task, we list the model architectures that have been implemented in vLLM.
Alongside each architecture, we include some popular models that use it.
## Model Implementation
### vLLM
If vLLM natively supports a model, its implementation can be found in <gh-file:vllm/model_executor/models>.
These models are what we list in <project:#supported-text-models> and <project:#supported-mm-models>.
(transformers-backend)=
### Transformers
vLLM also supports model implementations that are available in Transformers. This does not currently work for all models, but most decoder language models are supported, and vision language model support is planned!
To check if the modeling backend is Transformers, you can simply do this:
```python
from vllm import LLM
llm = LLM(model=..., task="generate") # Name or path of your model
llm.apply_model(lambda model: print(type(model)))
```
If it is `TransformersForCausalLM` then it means it's based on Transformers!
:::{tip}
You can force the use of `TransformersForCausalLM` by setting `model_impl="transformers"` for <project:#offline-inference> or `--model-impl transformers` for the <project:#openai-compatible-server>.
:::
:::{note}
vLLM may not fully optimise the Transformers implementation so you may see degraded performance if comparing a native model to a Transformers model in vLLM.
:::
#### Custom models
If a model is neither supported natively by vLLM or Transformers, it can still be used in vLLM!
For a model to be compatible with the Transformers backend for vLLM it must:
- be a Transformers compatible custom model (see [Transformers - Customizing models](https://huggingface.co/docs/transformers/en/custom_models)):
* The model directory must have the correct structure (e.g. `config.json` is present).
* `config.json` must contain `auto_map.AutoModel`.
- be a Transformers backend for vLLM compatible model (see <project:#writing-custom-models>):
* Customisation should be done in the base model (e.g. in `MyModel`, not `MyModelForCausalLM`).
If the compatible model is:
- on the Hugging Face Model Hub, simply set `trust_remote_code=True` for <project:#offline-inference> or `--trust-remote-code` for the <project:#openai-compatible-server>.
- in a local directory, simply pass directory path to `model=<MODEL_DIR>` for <project:#offline-inference> or `vllm serve <MODEL_DIR>` for the <project:#openai-compatible-server>.
This means that, with the Transformers backend for vLLM, new models can be used before they are officially supported in Transformers or vLLM!
(writing-custom-models)=
#### Writing custom models
This section details the necessary modifications to make to a Transformers compatible custom model that make it compatible with the Transformers backend for vLLM. (We assume that a Transformers compatible custom model has already been created, see [Transformers - Customizing models](https://huggingface.co/docs/transformers/en/custom_models)).
To make your model compatible with the Transformers backend, it needs:
1. `kwargs` passed down through all modules from `MyModel` to `MyAttention`.
2. `MyAttention` must use `ALL_ATTENTION_FUNCTIONS` to call attention.
3. `MyModel` must contain `_supports_attention_backend = True`.
```{code-block} python
:caption: modeling_my_model.py
from transformers import PreTrainedModel
from torch import nn
class MyAttention(nn.Module):
def forward(self, hidden_states, **kwargs):
...
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
**kwargs,
)
...
class MyModel(PreTrainedModel):
_supports_attention_backend = True
```
Here is what happens in the background when this model is loaded:
1. The config is loaded.
2. `MyModel` Python class is loaded from the `auto_map` in config, and we check that the model `is_backend_compatible()`.
3. `MyModel` is loaded into `TransformersForCausalLM` (see <gh-file:vllm/model_executor/models/transformers.py>) which sets `self.config._attn_implementation = "vllm"` so that vLLM's attention layer is used.
That's it!
For your model to be compatible with vLLM's tensor parallel and/or pipeline parallel features, you must add `base_model_tp_plan` and/or `base_model_pp_plan` to your model's config class:
```{code-block} python
:caption: configuration_my_model.py
from transformers import PretrainedConfig
class MyConfig(PretrainedConfig):
base_model_tp_plan = {
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
```
- `base_model_tp_plan` is a `dict` that maps fully qualified layer name patterns to tensor parallel styles (currently only `"colwise"` and `"rowwise"` are supported).
- `base_model_pp_plan` is a `dict` that maps direct child layer names to `tuple`s of `list`s of `str`s:
* You only need to do this for layers which are not present on all pipeline stages
* vLLM assumes that there will be only one `nn.ModuleList`, which is distributed across the pipeline stages
* The `list` in the first element of the `tuple` contains the names of the input arguments
* The `list` in the last element of the `tuple` contains the names of the variables the layer outputs to in your modeling code
## Loading a Model
### Hugging Face Hub
By default, vLLM loads models from [Hugging Face (HF) Hub](https://huggingface.co/models). To change the download path for models, you can set the `HF_HOME` environment variable; for more details, refer to [their official documentation](https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfhome).
To determine whether a given model is natively supported, you can check the `config.json` file inside the HF repository.
If the `"architectures"` field contains a model architecture listed below, then it should be natively supported.
Models do not _need_ to be natively supported to be used in vLLM.
The [Transformers backend](#transformers-backend) enables you to run models directly using their Transformers implementation (or even remote code on the Hugging Face Model Hub!).
:::{tip}
The easiest way to check if your model is really supported at runtime is to run the program below:
```python
from vllm import LLM
# For generative models (task=generate) only
llm = LLM(model=..., task="generate") # Name or path of your model
output = llm.generate("Hello, my name is")
print(output)
# For pooling models (task={embed,classify,reward,score}) only
llm = LLM(model=..., task="embed") # Name or path of your model
output = llm.encode("Hello, my name is")
print(output)
```
If vLLM successfully returns text (for generative models) or hidden states (for pooling models), it indicates that your model is supported.
:::
Otherwise, please refer to [Adding a New Model](#new-model) for instructions on how to implement your model in vLLM.
Alternatively, you can [open an issue on GitHub](https://github.com/vllm-project/vllm/issues/new/choose) to request vLLM support.
#### Download a model
If you prefer, you can use the Hugging Face CLI to [download a model](https://huggingface.co/docs/huggingface_hub/guides/cli#huggingface-cli-download) or specific files from a model repository:
```console
# Download a model
huggingface-cli download HuggingFaceH4/zephyr-7b-beta
# Specify a custom cache directory
huggingface-cli download HuggingFaceH4/zephyr-7b-beta --cache-dir ./path/to/cache
# Download a specific file from a model repo
huggingface-cli download HuggingFaceH4/zephyr-7b-beta eval_results.json
```
#### List the downloaded models
Use the Hugging Face CLI to [manage models](https://huggingface.co/docs/huggingface_hub/guides/manage-cache#scan-your-cache) stored in local cache:
```console
# List cached models
huggingface-cli scan-cache
# Show detailed (verbose) output
huggingface-cli scan-cache -v
# Specify a custom cache directory
huggingface-cli scan-cache --dir ~/.cache/huggingface/hub
```
#### Delete a cached model
Use the Hugging Face CLI to interactively [delete downloaded model](https://huggingface.co/docs/huggingface_hub/guides/manage-cache#clean-your-cache) from the cache:
```console
# The `delete-cache` command requires extra dependencies to work with the TUI.
# Please run `pip install huggingface_hub[cli]` to install them.
# Launch the interactive TUI to select models to delete
$ huggingface-cli delete-cache
? Select revisions to delete: 1 revisions selected counting for 438.9M.
○ None of the following (if selected, nothing will be deleted).
Model BAAI/bge-base-en-v1.5 (438.9M, used 1 week ago)
❯ ◉ a5beb1e3: main # modified 1 week ago
Model BAAI/bge-large-en-v1.5 (1.3G, used 1 week ago)
○ d4aa6901: main # modified 1 week ago
Model BAAI/bge-reranker-base (1.1G, used 4 weeks ago)
○ 2cfc18c9: main # modified 4 weeks ago
Press <space> to select, <enter> to validate and <ctrl+c> to quit without modification.
# Need to confirm after selected
? Select revisions to delete: 1 revision(s) selected.
? 1 revisions selected counting for 438.9M. Confirm deletion ? Yes
Start deletion.
Done. Deleted 1 repo(s) and 0 revision(s) for a total of 438.9M.
```
#### Using a proxy
Here are some tips for loading/downloading models from Hugging Face using a proxy:
- Set the proxy globally for your session (or set it in the profile file):
```shell
export http_proxy=http://your.proxy.server:port
export https_proxy=http://your.proxy.server:port
```
- Set the proxy for just the current command:
```shell
https_proxy=http://your.proxy.server:port huggingface-cli download <model_name>
# or use vllm cmd directly
https_proxy=http://your.proxy.server:port vllm serve <model_name> --disable-log-requests
```
- Set the proxy in Python interpreter:
```python
import os
os.environ['http_proxy'] = 'http://your.proxy.server:port'
os.environ['https_proxy'] = 'http://your.proxy.server:port'
```
### ModelScope
To use models from [ModelScope](https://www.modelscope.cn) instead of Hugging Face Hub, set an environment variable:
```shell
export VLLM_USE_MODELSCOPE=True
```
And use with `trust_remote_code=True`.
```python
from vllm import LLM
llm = LLM(model=..., revision=..., task=..., trust_remote_code=True)
# For generative models (task=generate) only
output = llm.generate("Hello, my name is")
print(output)
# For pooling models (task={embed,classify,reward,score}) only
output = llm.encode("Hello, my name is")
print(output)
```
(feature-status-legend)=
## Feature Status Legend
- ✅︎ indicates that the feature is supported for the model.
- 🚧 indicates that the feature is planned but not yet supported for the model.
- ⚠️ indicates that the feature is available but may have known issues or limitations.
(supported-text-models)=
## List of Text-only Language Models
### Generative Models
See [this page](#generative-models) for more information on how to use generative models.
#### Text Generation
Specified using `--task generate`.
:::{list-table}
:widths: 25 25 50 5 5
:header-rows: 1
- * Architecture
* Models
* Example HF Models
* [LoRA](#lora-adapter)
* [PP](#distributed-serving)
- * `AquilaForCausalLM`
* Aquila, Aquila2
* `BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc.
* ✅︎
* ✅︎
- * `ArcticForCausalLM`
* Arctic
* `Snowflake/snowflake-arctic-base`, `Snowflake/snowflake-arctic-instruct`, etc.
*
* ✅︎
- * `BaiChuanForCausalLM`
* Baichuan2, Baichuan
* `baichuan-inc/Baichuan2-13B-Chat`, `baichuan-inc/Baichuan-7B`, etc.
* ✅︎
* ✅︎
- * `BambaForCausalLM`
* Bamba
* `ibm-ai-platform/Bamba-9B-fp8`, `ibm-ai-platform/Bamba-9B`
*
*
- * `BloomForCausalLM`
* BLOOM, BLOOMZ, BLOOMChat
* `bigscience/bloom`, `bigscience/bloomz`, etc.
*
* ✅︎
- * `BartForConditionalGeneration`
* BART
* `facebook/bart-base`, `facebook/bart-large-cnn`, etc.
*
*
- * `ChatGLMModel`, `ChatGLMForConditionalGeneration`
* ChatGLM
* `THUDM/chatglm2-6b`, `THUDM/chatglm3-6b`, `ShieldLM-6B-chatglm3`, etc.
* ✅︎
* ✅︎
- * `CohereForCausalLM`, `Cohere2ForCausalLM`
* Command-R
* `CohereForAI/c4ai-command-r-v01`, `CohereForAI/c4ai-command-r7b-12-2024`, etc.
* ✅︎
* ✅︎
- * `DbrxForCausalLM`
* DBRX
* `databricks/dbrx-base`, `databricks/dbrx-instruct`, etc.
*
* ✅︎
- * `DeciLMForCausalLM`
* DeciLM
* `nvidia/Llama-3_3-Nemotron-Super-49B-v1`, etc.
*
* ✅︎
- * `DeepseekForCausalLM`
* DeepSeek
* `deepseek-ai/deepseek-llm-67b-base`, `deepseek-ai/deepseek-llm-7b-chat` etc.
*
* ✅︎
- * `DeepseekV2ForCausalLM`
* DeepSeek-V2
* `deepseek-ai/DeepSeek-V2`, `deepseek-ai/DeepSeek-V2-Chat` etc.
*
* ✅︎
- * `DeepseekV3ForCausalLM`
* DeepSeek-V3
* `deepseek-ai/DeepSeek-V3-Base`, `deepseek-ai/DeepSeek-V3` etc.
*
* ✅︎
- * `ExaoneForCausalLM`
* EXAONE-3
* `LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct`, etc.
* ✅︎
* ✅︎
- * `FalconForCausalLM`
* Falcon
* `tiiuae/falcon-7b`, `tiiuae/falcon-40b`, `tiiuae/falcon-rw-7b`, etc.
*
* ✅︎
- * `FalconMambaForCausalLM`
* FalconMamba
* `tiiuae/falcon-mamba-7b`, `tiiuae/falcon-mamba-7b-instruct`, etc.
* ✅︎
* ✅︎
- * `FalconH1ForCausalLM`
* Falcon-H1
* `tiiuae/Falcon-H1-34B-Base`, `tiiuae/Falcon-H1-34B-Instruct`, etc.
* ✅︎
* ✅︎
- * `GemmaForCausalLM`
* Gemma
* `google/gemma-2b`, `google/gemma-1.1-2b-it`, etc.
* ✅︎
* ✅︎
- * `Gemma2ForCausalLM`
* Gemma 2
* `google/gemma-2-9b`, `google/gemma-2-27b`, etc.
* ✅︎
* ✅︎
- * `Gemma3ForCausalLM`
* Gemma 3
* `google/gemma-3-1b-it`, etc.
* ✅︎
* ✅︎
- * `GlmForCausalLM`
* GLM-4
* `THUDM/glm-4-9b-chat-hf`, etc.
* ✅︎
* ✅︎
- * `Glm4ForCausalLM`
* GLM-4-0414
* `THUDM/GLM-4-32B-0414`, etc.
* ✅︎
* ✅︎
- * `GPT2LMHeadModel`
* GPT-2
* `gpt2`, `gpt2-xl`, etc.
*
* ✅︎
- * `GPTBigCodeForCausalLM`
* StarCoder, SantaCoder, WizardCoder
* `bigcode/starcoder`, `bigcode/gpt_bigcode-santacoder`, `WizardLM/WizardCoder-15B-V1.0`, etc.
* ✅︎
* ✅︎
- * `GPTJForCausalLM`
* GPT-J
* `EleutherAI/gpt-j-6b`, `nomic-ai/gpt4all-j`, etc.
*
* ✅︎
- * `GPTNeoXForCausalLM`
* GPT-NeoX, Pythia, OpenAssistant, Dolly V2, StableLM
* `EleutherAI/gpt-neox-20b`, `EleutherAI/pythia-12b`, `OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5`, `databricks/dolly-v2-12b`, `stabilityai/stablelm-tuned-alpha-7b`, etc.
*
* ✅︎
- * `GraniteForCausalLM`
* Granite 3.0, Granite 3.1, PowerLM
* `ibm-granite/granite-3.0-2b-base`, `ibm-granite/granite-3.1-8b-instruct`, `ibm/PowerLM-3b`, etc.
* ✅︎
* ✅︎
- * `GraniteMoeForCausalLM`
* Granite 3.0 MoE, PowerMoE
* `ibm-granite/granite-3.0-1b-a400m-base`, `ibm-granite/granite-3.0-3b-a800m-instruct`, `ibm/PowerMoE-3b`, etc.
* ✅︎
* ✅︎
- * `GraniteMoeHybridForCausalLM`
* Granite 4.0 MoE Hybrid
* `ibm-granite/granite-4.0-tiny-preview`, etc.
* ✅︎
* ✅︎
- * `GraniteMoeSharedForCausalLM`
* Granite MoE Shared
* `ibm-research/moe-7b-1b-active-shared-experts` (test model)
* ✅︎
* ✅︎
- * `GritLM`
* GritLM
* `parasail-ai/GritLM-7B-vllm`.
* ✅︎
* ✅︎
- * `Grok1ModelForCausalLM`
* Grok1
* `hpcai-tech/grok-1`.
* ✅︎
* ✅︎
- * `InternLMForCausalLM`
* InternLM
* `internlm/internlm-7b`, `internlm/internlm-chat-7b`, etc.
* ✅︎
* ✅︎
- * `InternLM2ForCausalLM`
* InternLM2
* `internlm/internlm2-7b`, `internlm/internlm2-chat-7b`, etc.
* ✅︎
* ✅︎
- * `InternLM3ForCausalLM`
* InternLM3
* `internlm/internlm3-8b-instruct`, etc.
* ✅︎
* ✅︎
- * `JAISLMHeadModel`
* Jais
* `inceptionai/jais-13b`, `inceptionai/jais-13b-chat`, `inceptionai/jais-30b-v3`, `inceptionai/jais-30b-chat-v3`, etc.
*
* ✅︎
- * `JambaForCausalLM`
* Jamba
* `ai21labs/AI21-Jamba-1.5-Large`, `ai21labs/AI21-Jamba-1.5-Mini`, `ai21labs/Jamba-v0.1`, etc.
* ✅︎
* ✅︎
- * `LlamaForCausalLM`
* Llama 3.1, Llama 3, Llama 2, LLaMA, Yi
* `meta-llama/Meta-Llama-3.1-405B-Instruct`, `meta-llama/Meta-Llama-3.1-70B`, `meta-llama/Meta-Llama-3-70B-Instruct`, `meta-llama/Llama-2-70b-hf`, `01-ai/Yi-34B`, etc.
* ✅︎
* ✅︎
- * `MambaForCausalLM`
* Mamba
* `state-spaces/mamba-130m-hf`, `state-spaces/mamba-790m-hf`, `state-spaces/mamba-2.8b-hf`, etc.
*
* ✅︎
- * `MiniCPMForCausalLM`
* MiniCPM
* `openbmb/MiniCPM-2B-sft-bf16`, `openbmb/MiniCPM-2B-dpo-bf16`, `openbmb/MiniCPM-S-1B-sft`, etc.
* ✅︎
* ✅︎
- * `MiniCPM3ForCausalLM`
* MiniCPM3
* `openbmb/MiniCPM3-4B`, etc.
* ✅︎
* ✅︎
- * `MistralForCausalLM`
* Mistral, Mistral-Instruct
* `mistralai/Mistral-7B-v0.1`, `mistralai/Mistral-7B-Instruct-v0.1`, etc.
* ✅︎
* ✅︎
- * `MixtralForCausalLM`
* Mixtral-8x7B, Mixtral-8x7B-Instruct
* `mistralai/Mixtral-8x7B-v0.1`, `mistralai/Mixtral-8x7B-Instruct-v0.1`, `mistral-community/Mixtral-8x22B-v0.1`, etc.
* ✅︎
* ✅︎
- * `MPTForCausalLM`
* MPT, MPT-Instruct, MPT-Chat, MPT-StoryWriter
* `mosaicml/mpt-7b`, `mosaicml/mpt-7b-storywriter`, `mosaicml/mpt-30b`, etc.
*
* ✅︎
- * `NemotronForCausalLM`
* Nemotron-3, Nemotron-4, Minitron
* `nvidia/Minitron-8B-Base`, `mgoin/Nemotron-4-340B-Base-hf-FP8`, etc.
* ✅︎
* ✅︎
- * `OLMoForCausalLM`
* OLMo
* `allenai/OLMo-1B-hf`, `allenai/OLMo-7B-hf`, etc.
*
* ✅︎
- * `OLMo2ForCausalLM`
* OLMo2
* `allenai/OLMo-2-0425-1B`, etc.
*
* ✅︎
- * `OLMoEForCausalLM`
* OLMoE
* `allenai/OLMoE-1B-7B-0924`, `allenai/OLMoE-1B-7B-0924-Instruct`, etc.
* ✅︎
* ✅︎
- * `OPTForCausalLM`
* OPT, OPT-IML
* `facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc.
*
* ✅︎
- * `OrionForCausalLM`
* Orion
* `OrionStarAI/Orion-14B-Base`, `OrionStarAI/Orion-14B-Chat`, etc.
*
* ✅︎
- * `PhiForCausalLM`
* Phi
* `microsoft/phi-1_5`, `microsoft/phi-2`, etc.
* ✅︎
* ✅︎
- * `Phi3ForCausalLM`
* Phi-4, Phi-3
* `microsoft/Phi-4-mini-instruct`, `microsoft/Phi-4`, `microsoft/Phi-3-mini-4k-instruct`, `microsoft/Phi-3-mini-128k-instruct`, `microsoft/Phi-3-medium-128k-instruct`, etc.
* ✅︎
* ✅︎
- * `Phi3SmallForCausalLM`
* Phi-3-Small
* `microsoft/Phi-3-small-8k-instruct`, `microsoft/Phi-3-small-128k-instruct`, etc.
*
* ✅︎
- * `PhiMoEForCausalLM`
* Phi-3.5-MoE
* `microsoft/Phi-3.5-MoE-instruct`, etc.
* ✅︎
* ✅︎
- * `PersimmonForCausalLM`
* Persimmon
* `adept/persimmon-8b-base`, `adept/persimmon-8b-chat`, etc.
*
* ✅︎
- * `Plamo2ForCausalLM`
* PLaMo2
* `pfnet/plamo-2-1b`, `pfnet/plamo-2-8b`, etc.
*
*
- * `QWenLMHeadModel`
* Qwen
* `Qwen/Qwen-7B`, `Qwen/Qwen-7B-Chat`, etc.
* ✅︎
* ✅︎
- * `Qwen2ForCausalLM`
* QwQ, Qwen2
* `Qwen/QwQ-32B-Preview`, `Qwen/Qwen2-7B-Instruct`, `Qwen/Qwen2-7B`, etc.
* ✅︎
* ✅︎
- * `Qwen2MoeForCausalLM`
* Qwen2MoE
* `Qwen/Qwen1.5-MoE-A2.7B`, `Qwen/Qwen1.5-MoE-A2.7B-Chat`, etc.
*
* ✅︎
- * `Qwen3ForCausalLM`
* Qwen3
* `Qwen/Qwen3-8B`, etc.
* ✅︎
* ✅︎
- * `Qwen3MoeForCausalLM`
* Qwen3MoE
* `Qwen/Qwen3-30B-A3B`, etc.
*
* ✅︎
- * `StableLmForCausalLM`
* StableLM
* `stabilityai/stablelm-3b-4e1t`, `stabilityai/stablelm-base-alpha-7b-v2`, etc.
*
* ✅︎
- * `Starcoder2ForCausalLM`
* Starcoder2
* `bigcode/starcoder2-3b`, `bigcode/starcoder2-7b`, `bigcode/starcoder2-15b`, etc.
*
* ✅︎
- * `SolarForCausalLM`
* Solar Pro
* `upstage/solar-pro-preview-instruct`, etc.
* ✅︎
* ✅︎
- * `TeleChat2ForCausalLM`
* TeleChat2
* `Tele-AI/TeleChat2-3B`, `Tele-AI/TeleChat2-7B`, `Tele-AI/TeleChat2-35B`, etc.
* ✅︎
* ✅︎
- * `TeleFLMForCausalLM`
* TeleFLM
* `CofeAI/FLM-2-52B-Instruct-2407`, `CofeAI/Tele-FLM`, etc.
* ✅︎
* ✅︎
- * `XverseForCausalLM`
* XVERSE
* `xverse/XVERSE-7B-Chat`, `xverse/XVERSE-13B-Chat`, `xverse/XVERSE-65B-Chat`, etc.
* ✅︎
* ✅︎
- * `MiniMaxText01ForCausalLM`
* MiniMax-Text
* `MiniMaxAI/MiniMax-Text-01`, etc.
*
* ✅︎
- * `Zamba2ForCausalLM`
* Zamba2
* `Zyphra/Zamba2-7B-instruct`, `Zyphra/Zamba2-2.7B-instruct`, `Zyphra/Zamba2-1.2B-instruct`, etc.
*
*
- * `MiMoForCausalLM`
* MiMo
* `XiaomiMiMo/MiMo-7B-RL`, etc.
*
*
:::
:::{note}
Currently, the ROCm version of vLLM supports Mistral and Mixtral only for context lengths up to 4096.
:::
### Pooling Models
See [this page](pooling-models) for more information on how to use pooling models.
:::{important}
Since some model architectures support both generative and pooling tasks,
you should explicitly specify the task type to ensure that the model is used in pooling mode instead of generative mode.
:::
#### Text Embedding
Specified using `--task embed`.
:::{list-table}
:widths: 25 25 50 5 5
:header-rows: 1
- * Architecture
* Models
* Example HF Models
* [LoRA](#lora-adapter)
* [PP](#distributed-serving)
- * `BertModel`
* BERT-based
* `BAAI/bge-base-en-v1.5`, `Snowflake/snowflake-arctic-embed-xs`, etc.
*
*
- * `Gemma2Model`
* Gemma 2-based
* `BAAI/bge-multilingual-gemma2`, etc.
*
* ✅︎
- * `GritLM`
* GritLM
* `parasail-ai/GritLM-7B-vllm`.
* ✅︎
* ✅︎
- * `GteModel`
* Arctic-Embed-2.0-M
* `Snowflake/snowflake-arctic-embed-m-v2.0`.
*
* ︎
- * `GteNewModel`
* mGTE-TRM (see note)
* `Alibaba-NLP/gte-multilingual-base`, etc.
* ︎
* ︎
- * `ModernBertModel`
* ModernBERT-based
* `Alibaba-NLP/gte-modernbert-base`, etc.
* ︎
* ︎
- * `NomicBertModel`
* Nomic BERT
* `nomic-ai/nomic-embed-text-v1`, `nomic-ai/nomic-embed-text-v2-moe`, `Snowflake/snowflake-arctic-embed-m-long`, etc.
* ︎
* ︎
- * `LlamaModel`, `LlamaForCausalLM`, `MistralModel`, etc.
* Llama-based
* `intfloat/e5-mistral-7b-instruct`, etc.
* ✅︎
* ✅︎
- * `Qwen2Model`, `Qwen2ForCausalLM`
* Qwen2-based
* `ssmits/Qwen2-7B-Instruct-embed-base` (see note), `Alibaba-NLP/gte-Qwen2-7B-instruct` (see note), etc.
* ✅︎
* ✅︎
- * `RobertaModel`, `RobertaForMaskedLM`
* RoBERTa-based
* `sentence-transformers/all-roberta-large-v1`, etc.
*
*
- * `XLMRobertaModel`
* XLM-RoBERTa-based
* `intfloat/multilingual-e5-large`, `jinaai/jina-reranker-v2-base-multilingual`, `Snowflake/snowflake-arctic-embed-l-v2.0`, `jinaai/jina-embeddings-v3`(see note), etc.
*
*
:::
:::{note}
`ssmits/Qwen2-7B-Instruct-embed-base` has an improperly defined Sentence Transformers config.
You should manually set mean pooling by passing `--override-pooler-config '{"pooling_type": "MEAN"}'`.
:::
:::{note}
The HF implementation of `Alibaba-NLP/gte-Qwen2-1.5B-instruct` is hardcoded to use causal attention despite what is shown in `config.json`. To compare vLLM vs HF results,
you should set `--hf-overrides '{"is_causal": true}'` in vLLM so that the two implementations are consistent with each other.
For both the 1.5B and 7B variants, you also need to enable `--trust-remote-code` for the correct tokenizer to be loaded.
See [relevant issue on HF Transformers](https://github.com/huggingface/transformers/issues/34882).
:::
:::{note}
`jinaai/jina-embeddings-v3` supports multiple tasks through lora, while vllm temporarily only supports text-matching tasks by merging lora weights.
:::
:::{note}
The second-generation GTE model (mGTE-TRM) is named `NewModel`. The name `NewModel` is too generic, you should set `--hf-overrides '{"architectures": ["GteNewModel"]}'` to specify the use of the `GteNewModel` architecture.
:::
If your model is not in the above list, we will try to automatically convert the model using
{func}`~vllm.model_executor.models.adapters.as_embedding_model`. By default, the embeddings
of the whole prompt are extracted from the normalized hidden state corresponding to the last token.
#### Reward Modeling
Specified using `--task reward`.
:::{list-table}
:widths: 25 25 50 5 5
:header-rows: 1
- * Architecture
* Models
* Example HF Models
* [LoRA](#lora-adapter)
* [PP](#distributed-serving)
- * `InternLM2ForRewardModel`
* InternLM2-based
* `internlm/internlm2-1_8b-reward`, `internlm/internlm2-7b-reward`, etc.
* ✅︎
* ✅︎
- * `LlamaForCausalLM`
* Llama-based
* `peiyi9979/math-shepherd-mistral-7b-prm`, etc.
* ✅︎
* ✅︎
- * `Qwen2ForRewardModel`
* Qwen2-based
* `Qwen/Qwen2.5-Math-RM-72B`, etc.
* ✅︎
* ✅︎
- * `Qwen2ForProcessRewardModel`
* Qwen2-based
* `Qwen/Qwen2.5-Math-PRM-7B`, `Qwen/Qwen2.5-Math-PRM-72B`, etc.
* ✅︎
* ✅︎
:::
If your model is not in the above list, we will try to automatically convert the model using
{func}`~vllm.model_executor.models.adapters.as_reward_model`. By default, we return the hidden states of each token directly.
:::{important}
For process-supervised reward models such as `peiyi9979/math-shepherd-mistral-7b-prm`, the pooling config should be set explicitly,
e.g.: `--override-pooler-config '{"pooling_type": "STEP", "step_tag_id": 123, "returned_token_ids": [456, 789]}'`.
:::
#### Classification
Specified using `--task classify`.
:::{list-table}
:widths: 25 25 50 5 5
:header-rows: 1
- * Architecture
* Models
* Example HF Models
* [LoRA](#lora-adapter)
* [PP](#distributed-serving)
- * `JambaForSequenceClassification`
* Jamba
* `ai21labs/Jamba-tiny-reward-dev`, etc.
* ✅︎
* ✅︎
- * `Qwen2ForSequenceClassification`
* Qwen2-based
* `jason9693/Qwen2.5-1.5B-apeach`, etc.
* ✅︎
* ✅︎
:::
If your model is not in the above list, we will try to automatically convert the model using
{func}`~vllm.model_executor.models.adapters.as_classification_model`. By default, the class probabilities are extracted from the softmaxed hidden state corresponding to the last token.
#### Sentence Pair Scoring
Specified using `--task score`.
:::{list-table}
:widths: 25 25 50 5 5
:header-rows: 1
- * Architecture
* Models
* Example HF Models
* [LoRA](#lora-adapter)
* [PP](#distributed-serving)
- * `BertForSequenceClassification`
* BERT-based
* `cross-encoder/ms-marco-MiniLM-L-6-v2`, etc.
*
*
- * `RobertaForSequenceClassification`
* RoBERTa-based
* `cross-encoder/quora-roberta-base`, etc.
*
*
- * `XLMRobertaForSequenceClassification`
* XLM-RoBERTa-based
* `BAAI/bge-reranker-v2-m3`, etc.
*
*
- * `ModernBertForSequenceClassification`
* ModernBert-based
* `Alibaba-NLP/gte-reranker-modernbert-base`, etc.
*
*
:::
(supported-mm-models)=
## List of Multimodal Language Models
The following modalities are supported depending on the model:
- **T**ext
- **I**mage
- **V**ideo
- **A**udio
Any combination of modalities joined by `+` are supported.
- e.g.: `T + I` means that the model supports text-only, image-only, and text-with-image inputs.
On the other hand, modalities separated by `/` are mutually exclusive.
- e.g.: `T / I` means that the model supports text-only and image-only inputs, but not text-with-image inputs.
See [this page](#multimodal-inputs) on how to pass multi-modal inputs to the model.
:::{important}
**To enable multiple multi-modal items per text prompt in vLLM V0**, you have to set `limit_mm_per_prompt` (offline inference)
or `--limit-mm-per-prompt` (online serving). For example, to enable passing up to 4 images per text prompt:
Offline inference:
```python
from vllm import LLM
llm = LLM(
model="Qwen/Qwen2-VL-7B-Instruct",
limit_mm_per_prompt={"image": 4},
)
```
Online serving:
```bash
vllm serve Qwen/Qwen2-VL-7B-Instruct --limit-mm-per-prompt '{"image":4}'
```
**This is no longer required if you are using vLLM V1.**
:::
:::{note}
vLLM currently only supports adding LoRA to the language backbone of multimodal models.
:::
### Generative Models
See [this page](#generative-models) for more information on how to use generative models.
#### Text Generation
Specified using `--task generate`.
:::{list-table}
:widths: 25 25 15 20 5 5 5
:header-rows: 1
- * Architecture
* Models
* Inputs
* Example HF Models
* [LoRA](#lora-adapter)
* [PP](#distributed-serving)
* [V1](gh-issue:8779)
- * `AriaForConditionalGeneration`
* Aria
* T + I<sup>+</sup>
* `rhymes-ai/Aria`
*
* ✅︎
* ✅︎
- * `AyaVisionForConditionalGeneration`
* Aya Vision
* T + I<sup>+</sup>
* `CohereForAI/aya-vision-8b`, `CohereForAI/aya-vision-32b`, etc.
*
* ✅︎
* ✅︎
- * `Blip2ForConditionalGeneration`
* BLIP-2
* T + I<sup>E</sup>
* `Salesforce/blip2-opt-2.7b`, `Salesforce/blip2-opt-6.7b`, etc.
*
* ✅︎
* ✅︎
- * `ChameleonForConditionalGeneration`
* Chameleon
* T + I
* `facebook/chameleon-7b` etc.
*
* ✅︎
* ✅︎
- * `DeepseekVLV2ForCausalLM`<sup>^</sup>
* DeepSeek-VL2
* T + I<sup>+</sup>
* `deepseek-ai/deepseek-vl2-tiny`, `deepseek-ai/deepseek-vl2-small`, `deepseek-ai/deepseek-vl2` etc.
*
* ✅︎
* ✅︎
- * `Florence2ForConditionalGeneration`
* Florence-2
* T + I
* `microsoft/Florence-2-base`, `microsoft/Florence-2-large` etc.
*
*
*
- * `FuyuForCausalLM`
* Fuyu
* T + I
* `adept/fuyu-8b` etc.
*
* ✅︎
* ✅︎
- * `Gemma3ForConditionalGeneration`
* Gemma 3
* T + I<sup>+</sup>
* `google/gemma-3-4b-it`, `google/gemma-3-27b-it`, etc.
* ✅︎
* ✅︎
* ⚠️
- * `GLM4VForCausalLM`<sup>^</sup>
* GLM-4V
* T + I
* `THUDM/glm-4v-9b`, `THUDM/cogagent-9b-20241220` etc.
* ✅︎
* ✅︎
* ✅︎
- * `GraniteSpeechForConditionalGeneration`
* Granite Speech
* T + A
* `ibm-granite/granite-speech-3.3-8b`
* ✅︎
* ✅︎
* ✅︎
- * `H2OVLChatModel`
* H2OVL
* T + I<sup>E+</sup>
* `h2oai/h2ovl-mississippi-800m`, `h2oai/h2ovl-mississippi-2b`, etc.
*
* ✅︎
* ✅︎\*
- * `Idefics3ForConditionalGeneration`
* Idefics3
* T + I
* `HuggingFaceM4/Idefics3-8B-Llama3` etc.
* ✅︎
*
* ✅︎
- * `InternVLChatModel`
* InternVL 3.0, InternVideo 2.5, InternVL 2.5, Mono-InternVL, InternVL 2.0
* T + I<sup>E+</sup>
* `OpenGVLab/InternVL3-9B`, `OpenGVLab/InternVideo2_5_Chat_8B`, `OpenGVLab/InternVL2_5-4B`, `OpenGVLab/Mono-InternVL-2B`, `OpenGVLab/InternVL2-4B`, etc.
*
* ✅︎
* ✅︎
- * `KimiVLForConditionalGeneration`
* Kimi-VL-A3B-Instruct, Kimi-VL-A3B-Thinking
* T + I<sup>+</sup>
* `moonshotai/Kimi-VL-A3B-Instruct`, `moonshotai/Kimi-VL-A3B-Thinking`
*
*
* ✅︎
- * `Llama4ForConditionalGeneration`
* Llama 4
* T + I<sup>+</sup>
* `meta-llama/Llama-4-Scout-17B-16E-Instruct`, `meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8`, `meta-llama/Llama-4-Maverick-17B-128E-Instruct`, etc.
*
* ✅︎
* ✅︎
- * `LlavaForConditionalGeneration`
* LLaVA-1.5
* T + I<sup>E+</sup>
* `llava-hf/llava-1.5-7b-hf`, `TIGER-Lab/Mantis-8B-siglip-llama3` (see note), etc.
*
* ✅︎
* ✅︎
- * `LlavaNextForConditionalGeneration`
* LLaVA-NeXT
* T + I<sup>E+</sup>
* `llava-hf/llava-v1.6-mistral-7b-hf`, `llava-hf/llava-v1.6-vicuna-7b-hf`, etc.
*
* ✅︎
* ✅︎
- * `LlavaNextVideoForConditionalGeneration`
* LLaVA-NeXT-Video
* T + V
* `llava-hf/LLaVA-NeXT-Video-7B-hf`, etc.
*
* ✅︎
* ✅︎
- * `LlavaOnevisionForConditionalGeneration`
* LLaVA-Onevision
* T + I<sup>+</sup> + V<sup>+</sup>
* `llava-hf/llava-onevision-qwen2-7b-ov-hf`, `llava-hf/llava-onevision-qwen2-0.5b-ov-hf`, etc.
*
* ✅︎
* ✅︎
- * `MiniCPMO`
* MiniCPM-O
* T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>E+</sup>
* `openbmb/MiniCPM-o-2_6`, etc.
* ✅︎
* ✅︎
* ✅︎
- * `MiniCPMV`
* MiniCPM-V
* T + I<sup>E+</sup> + V<sup>E+</sup>
* `openbmb/MiniCPM-V-2` (see note), `openbmb/MiniCPM-Llama3-V-2_5`, `openbmb/MiniCPM-V-2_6`, etc.
* ✅︎
* ✅︎
* ✅︎
- * `MiniMaxVL01ForConditionalGeneration`
* MiniMax-VL
* T + I<sup>E+</sup>
* `MiniMaxAI/MiniMax-VL-01`, etc.
*
* ✅︎
* ✅︎
- * `Mistral3ForConditionalGeneration`
* Mistral3
* T + I<sup>+</sup>
* `mistralai/Mistral-Small-3.1-24B-Instruct-2503`, etc.
* ✅︎
* ✅︎
* ✅︎
- * `MllamaForConditionalGeneration`
* Llama 3.2
* T + I<sup>+</sup>
* `meta-llama/Llama-3.2-90B-Vision-Instruct`, `meta-llama/Llama-3.2-11B-Vision`, etc.
*
*
*
- * `MolmoForCausalLM`
* Molmo
* T + I<sup>+</sup>
* `allenai/Molmo-7B-D-0924`, `allenai/Molmo-7B-O-0924`, etc.
* ✅︎
* ✅︎
* ✅︎
- * `NVLM_D_Model`
* NVLM-D 1.0
* T + I<sup>+</sup>
* `nvidia/NVLM-D-72B`, etc.
*
* ✅︎
* ✅︎
- * `Ovis`
* Ovis2, Ovis1.6
* T + I<sup>+</sup>
* `AIDC-AI/Ovis2-1B`, `AIDC-AI/Ovis1.6-Llama3.2-3B`, etc.
*
*
* ✅︎
- * `PaliGemmaForConditionalGeneration`
* PaliGemma, PaliGemma 2
* T + I<sup>E</sup>
* `google/paligemma-3b-pt-224`, `google/paligemma-3b-mix-224`, `google/paligemma2-3b-ft-docci-448`, etc.
*
* ✅︎
* ⚠️
- * `Phi3VForCausalLM`
* Phi-3-Vision, Phi-3.5-Vision
* T + I<sup>E+</sup>
* `microsoft/Phi-3-vision-128k-instruct`, `microsoft/Phi-3.5-vision-instruct`, etc.
*
* ✅︎
* ✅︎
- * `Phi4MMForCausalLM`
* Phi-4-multimodal
* T + I<sup>+</sup> / T + A<sup>+</sup> / I<sup>+</sup> + A<sup>+</sup>
* `microsoft/Phi-4-multimodal-instruct`, etc.
* ✅︎
*
* ✅︎
- * `PixtralForConditionalGeneration`
* Pixtral
* T + I<sup>+</sup>
* `mistralai/Mistral-Small-3.1-24B-Instruct-2503`, `mistral-community/pixtral-12b`, etc.
*
* ✅︎
* ✅︎
- * `QwenVLForConditionalGeneration`<sup>^</sup>
* Qwen-VL
* T + I<sup>E+</sup>
* `Qwen/Qwen-VL`, `Qwen/Qwen-VL-Chat`, etc.
* ✅︎
* ✅︎
* ✅︎
- * `Qwen2AudioForConditionalGeneration`
* Qwen2-Audio
* T + A<sup>+</sup>
* `Qwen/Qwen2-Audio-7B-Instruct`
*
* ✅︎
* ✅︎
- * `Qwen2VLForConditionalGeneration`
* QVQ, Qwen2-VL
* T + I<sup>E+</sup> + V<sup>E+</sup>
* `Qwen/QVQ-72B-Preview`, `Qwen/Qwen2-VL-7B-Instruct`, `Qwen/Qwen2-VL-72B-Instruct`, etc.
* ✅︎
* ✅︎
* ✅︎
- * `Qwen2_5_VLForConditionalGeneration`
* Qwen2.5-VL
* T + I<sup>E+</sup> + V<sup>E+</sup>
* `Qwen/Qwen2.5-VL-3B-Instruct`, `Qwen/Qwen2.5-VL-72B-Instruct`, etc.
* ✅︎
* ✅︎
* ✅︎
- * `Qwen2_5OmniThinkerForConditionalGeneration`
* Qwen2.5-Omni
* T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup>
* `Qwen/Qwen2.5-Omni-7B`
*
* ✅︎
* ✅︎\*
- * `SkyworkR1VChatModel`
* Skywork-R1V-38B
* T + I
* `Skywork/Skywork-R1V-38B`
*
* ✅︎
* ✅︎
- * `SmolVLMForConditionalGeneration`
* SmolVLM2
* T + I
* `SmolVLM2-2.2B-Instruct`
*
* ✅︎
* ✅︎
- * `UltravoxModel`
* Ultravox
* T + A<sup>E+</sup>
* `fixie-ai/ultravox-v0_5-llama-3_2-1b`
* ✅︎
* ✅︎
* ✅︎
:::
<sup>^</sup> You need to set the architecture name via `--hf-overrides` to match the one in vLLM.
&nbsp;&nbsp;&nbsp;&nbsp;• For example, to use DeepSeek-VL2 series models:
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`--hf-overrides '{"architectures": ["DeepseekVLV2ForCausalLM"]}'`
<sup>E</sup> Pre-computed embeddings can be inputted for this modality.
<sup>+</sup> Multiple items can be inputted per text prompt for this modality.
:::{warning}
Both V0 and V1 support `Gemma3ForConditionalGeneration` for text-only inputs.
However, there are differences in how they handle text + image inputs:
V0 correctly implements the model's attention pattern:
- Uses bidirectional attention between the image tokens corresponding to the same image
- Uses causal attention for other tokens
- Implemented via (naive) PyTorch SDPA with masking tensors
- Note: May use significant memory for long prompts with image
V1 currently uses a simplified attention pattern:
- Uses causal attention for all tokens, including image tokens
- Generates reasonable outputs but does not match the original model's attention for text + image inputs, especially when `{"do_pan_and_scan": true}`
- Will be updated in the future to support the correct behavior
This limitation exists because the model's mixed attention pattern (bidirectional for images, causal otherwise) is not yet supported by vLLM's attention backends.
:::
:::{note}
`h2oai/h2ovl-mississippi-2b` will be available in V1 once we support head size 80.
:::
:::{note}
To use `TIGER-Lab/Mantis-8B-siglip-llama3`, you have to pass `--hf_overrides '{"architectures": ["MantisForConditionalGeneration"]}'` when running vLLM.
:::
:::{warning}
The output quality of `AllenAI/Molmo-7B-D-0924` (especially in object localization tasks) has deteriorated in recent updates.
For the best results, we recommend using the following dependency versions (tested on A10 and L40):
```text
# Core vLLM-compatible dependencies with Molmo accuracy setup (tested on L40)
torch==2.5.1
torchvision==0.20.1
transformers==4.48.1
tokenizers==0.21.0
tiktoken==0.7.0
vllm==0.7.0
# Optional but recommended for improved performance and stability
triton==3.1.0
xformers==0.0.28.post3
uvloop==0.21.0
protobuf==5.29.3
openai==1.60.2
opencv-python-headless==4.11.0.86
pillow==10.4.0
# Installed FlashAttention (for float16 only)
flash-attn>=2.5.6 # Not used in float32, but should be documented
```
**Note:** Make sure you understand the security implications of using outdated packages.
:::
:::{note}
The official `openbmb/MiniCPM-V-2` doesn't work yet, so we need to use a fork (`HwwwH/MiniCPM-V-2`) for now.
For more details, please see: <gh-pr:4087#issuecomment-2250397630>
:::
:::{warning}
Our PaliGemma implementations have the same problem as Gemma 3 (see above) for both V0 and V1.
:::
:::{note}
To use Qwen2.5-Omni, you have to install Hugging Face Transformers library from source via
`pip install git+https://github.com/huggingface/transformers.git`.
Read audio from video pre-processing is currently supported on V0 (but not V1), because overlapping modalities is not yet supported in V1.
`--mm-processor-kwargs '{"use_audio_in_video": true}'`.
:::
### Pooling Models
See [this page](pooling-models) for more information on how to use pooling models.
:::{important}
Since some model architectures support both generative and pooling tasks,
you should explicitly specify the task type to ensure that the model is used in pooling mode instead of generative mode.
:::
#### Text Embedding
Specified using `--task embed`.
Any text generation model can be converted into an embedding model by passing `--task embed`.
:::{note}
To get the best results, you should use pooling models that are specifically trained as such.
:::
The following table lists those that are tested in vLLM.
:::{list-table}
:widths: 25 25 15 25 5 5
:header-rows: 1
- * Architecture
* Models
* Inputs
* Example HF Models
* [LoRA](#lora-adapter)
* [PP](#distributed-serving)
- * `LlavaNextForConditionalGeneration`
* LLaVA-NeXT-based
* T / I
* `royokong/e5-v`
*
* ✅︎
- * `Phi3VForCausalLM`
* Phi-3-Vision-based
* T + I
* `TIGER-Lab/VLM2Vec-Full`
* 🚧
* ✅︎
- * `Qwen2VLForConditionalGeneration`
* Qwen2-VL-based
* T + I
* `MrLight/dse-qwen2-2b-mrl-v1`
*
* ✅︎
:::
#### Transcription
Specified using `--task transcription`.
Speech2Text models trained specifically for Automatic Speech Recognition.
:::{list-table}
:widths: 25 25 25 5 5
:header-rows: 1
- * Architecture
* Models
* Example HF Models
* [LoRA](#lora-adapter)
* [PP](#distributed-serving)
- * `Whisper`
* Whisper-based
* `openai/whisper-large-v3-turbo`
* 🚧
* 🚧
:::
_________________
## Model Support Policy
At vLLM, we are committed to facilitating the integration and support of third-party models within our ecosystem. Our approach is designed to balance the need for robustness and the practical limitations of supporting a wide range of models. Here’s how we manage third-party model support:
1. **Community-Driven Support**: We encourage community contributions for adding new models. When a user requests support for a new model, we welcome pull requests (PRs) from the community. These contributions are evaluated primarily on the sensibility of the output they generate, rather than strict consistency with existing implementations such as those in transformers. **Call for contribution:** PRs coming directly from model vendors are greatly appreciated!
2. **Best-Effort Consistency**: While we aim to maintain a level of consistency between the models implemented in vLLM and other frameworks like transformers, complete alignment is not always feasible. Factors like acceleration techniques and the use of low-precision computations can introduce discrepancies. Our commitment is to ensure that the implemented models are functional and produce sensible results.
:::{tip}
When comparing the output of `model.generate` from Hugging Face Transformers with the output of `llm.generate` from vLLM, note that the former reads the model's generation config file (i.e., [generation_config.json](https://github.com/huggingface/transformers/blob/19dabe96362803fb0a9ae7073d03533966598b17/src/transformers/generation/utils.py#L1945)) and applies the default parameters for generation, while the latter only uses the parameters passed to the function. Ensure all sampling parameters are identical when comparing outputs.
:::
3. **Issue Resolution and Model Updates**: Users are encouraged to report any bugs or issues they encounter with third-party models. Proposed fixes should be submitted via PRs, with a clear explanation of the problem and the rationale behind the proposed solution. If a fix for one model impacts another, we rely on the community to highlight and address these cross-model dependencies. Note: for bugfix PRs, it is good etiquette to inform the original author to seek their feedback.
4. **Monitoring and Updates**: Users interested in specific models should monitor the commit history for those models (e.g., by tracking changes in the main/vllm/model_executor/models directory). This proactive approach helps users stay informed about updates and changes that may affect the models they use.
5. **Selective Focus**: Our resources are primarily directed towards models with significant user interest and impact. Models that are less frequently used may receive less attention, and we rely on the community to play a more active role in their upkeep and improvement.
Through this approach, vLLM fosters a collaborative environment where both the core development team and the broader community contribute to the robustness and diversity of the third-party models supported in our ecosystem.
Note that, as an inference engine, vLLM does not introduce new models. Therefore, all models supported by vLLM are third-party models in this regard.
We have the following levels of testing for models:
1. **Strict Consistency**: We compare the output of the model with the output of the model in the HuggingFace Transformers library under greedy decoding. This is the most stringent test. Please refer to [models tests](https://github.com/vllm-project/vllm/blob/main/tests/models) for the models that have passed this test.
2. **Output Sensibility**: We check if the output of the model is sensible and coherent, by measuring the perplexity of the output and checking for any obvious errors. This is a less stringent test.
3. **Runtime Functionality**: We check if the model can be loaded and run without errors. This is the least stringent test. Please refer to [functionality tests](gh-dir:tests) and [examples](gh-dir:examples) for the models that have passed this test.
4. **Community Feedback**: We rely on the community to provide feedback on the models. If a model is broken or not working as expected, we encourage users to raise issues to report it or open pull requests to fix it. The rest of the models fall under this category.
docs/source/serving/engine_args.md deleted 100644 → 0
View file @ d0bc2f81
(engine-args)=
# Engine Arguments
Engine arguments control the behavior of the vLLM engine.
- For [offline inference](#offline-inference), they are part of the arguments to `LLM` class.
- For [online serving](#openai-compatible-server), they are part of the arguments to `vllm serve`.
For references to all arguments available from `vllm serve` see the [serve args](#serve-args) documentation.
Below, you can find an explanation of every engine argument:
<!--- pyml disable-num-lines 7 no-space-in-emphasis -->
```{eval-rst}
.. argparse::
:module: vllm.engine.arg_utils
:func: _engine_args_parser
:prog: vllm serve
:nodefaultconst:
:markdownhelp:
```
## Async Engine Arguments
Additional arguments are available to the asynchronous engine which is used for online serving:
<!--- pyml disable-num-lines 7 no-space-in-emphasis -->
```{eval-rst}
.. argparse::
:module: vllm.engine.arg_utils
:func: _async_engine_args_parser
:prog: vllm serve
:nodefaultconst:
:markdownhelp:
```
docs/source/serving/env_vars.md deleted 100644 → 0
View file @ d0bc2f81
# Environment Variables
vLLM uses the following environment variables to configure the system:
:::{warning}
Please note that `VLLM_PORT` and `VLLM_HOST_IP` set the port and ip for vLLM's **internal usage**. It is not the port and ip for the API server. If you use `--host $VLLM_HOST_IP` and `--port $VLLM_PORT` to start the API server, it will not work.
All environment variables used by vLLM are prefixed with `VLLM_`. **Special care should be taken for Kubernetes users**: please do not name the service as `vllm`, otherwise environment variables set by Kubernetes might conflict with vLLM's environment variables, because [Kubernetes sets environment variables for each service with the capitalized service name as the prefix](https://kubernetes.io/docs/concepts/services-networking/service/#environment-variables).
:::
:::{literalinclude} ../../../vllm/envs.py
:end-before: end-env-vars-definition
:language: python
:start-after: begin-env-vars-definition
:::
docs/source/serving/integrations/index.md deleted 100644 → 0
View file @ d0bc2f81
# External Integrations
:::{toctree}
:maxdepth: 1
langchain
llamaindex
:::
docs/source/training/trl.md → docs/training/trl.md
View file @ a1fe24d9
......@@ -6,8 +6,7 @@ Online methods such as GRPO or Online DPO require the model to generate completi
See the guide [vLLM for fast generation in online methods](https://huggingface.co/docs/trl/main/en/speeding_up_training#vllm-for-fast-generation-in-online-methods) in the TRL documentation for more information.
:::{seealso}
For more information on the `use_vllm` flag you can provide to the configs of these online methods, see:
- [`trl.GRPOConfig.use_vllm`](https://huggingface.co/docs/trl/main/en/grpo_trainer#trl.GRPOConfig.use_vllm)
- [`trl.OnlineDPOConfig.use_vllm`](https://huggingface.co/docs/trl/main/en/online_dpo_trainer#trl.OnlineDPOConfig.use_vllm)
:::
!!! info
For more information on the `use_vllm` flag you can provide to the configs of these online methods, see:
- [`trl.GRPOConfig.use_vllm`](https://huggingface.co/docs/trl/main/en/grpo_trainer#trl.GRPOConfig.use_vllm)
- [`trl.OnlineDPOConfig.use_vllm`](https://huggingface.co/docs/trl/main/en/online_dpo_trainer#trl.OnlineDPOConfig.use_vllm)
mkdocs.yaml 0 → 100644
View file @ a1fe24d9
site_name: vLLM
site_url: https://docs.vllm.ai
repo_url: https://github.com/vllm-project/vllm
exclude_docs: |
*.inc.md
*.template.md
theme:
name: material
logo: assets/logos/vllm-logo-only-light.ico
favicon: assets/logos/vllm-logo-only-light.ico
palette:
# Palette toggle for automatic mode
- media: "(prefers-color-scheme)"
toggle:
icon: material/brightness-auto
name: Switch to light mode
# Palette toggle for light mode
- media: "(prefers-color-scheme: light)"
scheme: default
primary: white
toggle:
icon: material/brightness-7
name: Switch to dark mode
# Palette toggle for dark mode
- media: "(prefers-color-scheme: dark)"
scheme: slate
primary: black
toggle:
icon: material/brightness-2
name: Switch to system preference
features:
- content.code.copy
- content.tabs.link
- navigation.tracking
- navigation.tabs
- navigation.sections
- navigation.prune
- navigation.top
- search.highlight
- search.share
- toc.follow
custom_dir: docs/mkdocs/overrides
hooks:
- docs/mkdocs/hooks/remove_announcement.py
- docs/mkdocs/hooks/generate_examples.py
- docs/mkdocs/hooks/url_schemes.py
# Required to stop api-autonav from raising an error
# https://github.com/tlambert03/mkdocs-api-autonav/issues/16
nav:
- api
plugins:
- meta
- search
- autorefs
- awesome-nav
# For API reference generation
- api-autonav:
modules: ["vllm"]
api_root_uri: "api"
- mkdocstrings:
handlers:
python:
options:
show_symbol_type_heading: true
show_symbol_type_toc: true
summary:
modules: true
show_if_no_docstring: true
show_signature_annotations: true
separate_signature: true
show_overloads: true
signature_crossrefs: true
inventories:
- https://docs.python.org/3/objects.inv
- https://typing-extensions.readthedocs.io/en/latest/objects.inv
- https://docs.aiohttp.org/en/stable/objects.inv
- https://pillow.readthedocs.io/en/stable/objects.inv
- https://numpy.org/doc/stable/objects.inv
- https://pytorch.org/docs/stable/objects.inv
- https://psutil.readthedocs.io/en/stable/objects.inv
markdown_extensions:
- attr_list
- md_in_html
- admonition
- pymdownx.details
# For content tabs
- pymdownx.superfences
- pymdownx.tabbed:
slugify: !!python/object/apply:pymdownx.slugs.slugify
kwds:
case: lower
alternate_style: true
# For code highlighting
- pymdownx.highlight:
anchor_linenums: true
line_spans: __span
pygments_lang_class: true
- pymdownx.inlinehilite
- pymdownx.snippets
# For emoji and icons
- pymdownx.emoji:
emoji_index: !!python/name:material.extensions.emoji.twemoji
emoji_generator: !!python/name:material.extensions.emoji.to_svg
# For in page [TOC] (not sidebar)
- toc:
permalink: true
# For math rendering
- mdx_math:
enable_dollar_delimiter: true
extra_javascript:
- mkdocs/javascript/run_llm_widget.js
- https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_HTML
pyproject.toml
View file @ a1fe24d9
......@@ -165,9 +165,11 @@ markers = [
[tool.pymarkdown]
plugins.md004.style = "sublist" # ul-style
plugins.md007.indent = 4 # ul-indent
plugins.md013.enabled = false # line-length
plugins.md041.enabled = false # first-line-h1
plugins.md033.enabled = false # inline-html
plugins.md046.enabled = false # code-block-style
plugins.md024.allow_different_nesting = true # no-duplicate-headers
[tool.ty]
......
requirements/docs.txt
View file @ a1fe24d9
sphinx==7.4.7
sphinx-argparse==0.5.2
sphinx-book-theme==1.1.4
sphinx-copybutton==0.5.2
sphinx-design==0.6.1
sphinx-togglebutton==0.3.2
myst-parser==3.0.1 # `myst-parser==4.0.1` breaks inline code in titles
msgspec
snowballstemmer<3 # https://github.com/snowballstem/snowball/issues/229
commonmark # Required by sphinx-argparse when using :markdownhelp:
# Custom autodoc2 is necessary for faster docstring processing
# see: https://github.com/sphinx-extensions2/sphinx-autodoc2/issues/33#issuecomment-2856386035
git+https://github.com/hmellor/sphinx-autodoc2.git # sphinx-autodoc2==0.5.0
# packages to install to build the documentation
cachetools
-f https://download.pytorch.org/whl/cpu
torch
\ No newline at end of file
mkdocs
mkdocs-api-autonav
mkdocs-material
mkdocstrings-python
mkdocs-gen-files
mkdocs-awesome-nav
python-markdown-math
ruff
vllm/engine/llm_engine.py
View file @ a1fe24d9
......@@ -1263,12 +1263,10 @@ class LLMEngine:
def step(self) -> List[Union[RequestOutput, PoolingRequestOutput]]:
"""Performs one decoding iteration and returns newly generated results.
:::{figure} https://i.imgur.com/sv2HssD.png
:alt: Overview of the step function
:align: center
Overview of the step function.
:::
<figure markdown="span">
![Overview of the step function](https://i.imgur.com/sv2HssD.png)
<figcaption>Overview of the step function</figcaption>
</figure>
Details:
- Step 1: Schedules the sequences to be executed in the next
......
vllm/engine/metrics.py
View file @ a1fe24d9
......@@ -29,7 +29,7 @@ prometheus_client.disable_created_metrics()
# to extract the metrics definitions.
# begin-metrics-definitions
# --8<-- [start:metrics-definitions]
class Metrics:
"""
vLLM uses a multiprocessing-based frontend for the OpenAI server.
......@@ -293,7 +293,7 @@ class Metrics:
labelnames=labelnames))
# end-metrics-definitions
# --8<-- [end:metrics-definitions]
def _unregister_vllm_metrics(self) -> None:
for collector in list(prometheus_client.REGISTRY._collector_to_names):
......
vllm/entrypoints/llm.py
View file @ a1fe24d9
......@@ -131,10 +131,9 @@ class LLM:
**kwargs: Arguments for {class}`~vllm.EngineArgs`. (See
{ref}`engine-args`)
:::{note}
This class is intended to be used for offline inference. For online
serving, use the {class}`~vllm.AsyncLLMEngine` class instead.
:::
Note:
This class is intended to be used for offline inference. For online
serving, use the {class}`~vllm.AsyncLLMEngine` class instead.
"""
DEPRECATE_LEGACY: ClassVar[bool] = True
......@@ -422,11 +421,10 @@ class LLM:
A list of `RequestOutput` objects containing the
generated completions in the same order as the input prompts.
:::{note}
Using `prompts` and `prompt_token_ids` as keyword parameters is
considered legacy and may be deprecated in the future. You should
instead pass them via the `inputs` parameter.
:::
Note:
Using `prompts` and `prompt_token_ids` as keyword parameters is
considered legacy and may be deprecated in the future. You should
instead pass them via the `inputs` parameter.
"""
runner_type = self.llm_engine.model_config.runner_type
if runner_type not in ["generate", "transcription"]:
......@@ -502,10 +500,9 @@ class LLM:
Returns:
A list containing the results from each worker.
:::{note}
It is recommended to use this API to only pass control messages,
and set up data-plane communication to pass data.
:::
Note:
It is recommended to use this API to only pass control messages,
and set up data-plane communication to pass data.
"""
return self.llm_engine.collective_rpc(method, timeout, args, kwargs)
......@@ -924,11 +921,10 @@ class LLM:
A list of `PoolingRequestOutput` objects containing the
pooled hidden states in the same order as the input prompts.
:::{note}
Using `prompts` and `prompt_token_ids` as keyword parameters is
considered legacy and may be deprecated in the future. You should
instead pass them via the `inputs` parameter.
:::
Note:
Using `prompts` and `prompt_token_ids` as keyword parameters is
considered legacy and may be deprecated in the future. You should
instead pass them via the `inputs` parameter.
"""
runner_type = self.llm_engine.model_config.runner_type
if runner_type != "pooling":
......
vllm/entrypoints/openai/protocol.py
View file @ a1fe24d9
......@@ -251,7 +251,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
parallel_tool_calls: Optional[bool] = False
user: Optional[str] = None
# doc: begin-chat-completion-sampling-params
# --8<-- [start:chat-completion-sampling-params]
best_of: Optional[int] = None
use_beam_search: bool = False
top_k: Optional[int] = None
......@@ -266,9 +266,9 @@ class ChatCompletionRequest(OpenAIBaseModel):
spaces_between_special_tokens: bool = True
truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
prompt_logprobs: Optional[int] = None
# doc: end-chat-completion-sampling-params
# --8<-- [end:chat-completion-sampling-params]
# doc: begin-chat-completion-extra-params
# --8<-- [start:chat-completion-extra-params]
echo: bool = Field(
default=False,
description=(
......@@ -407,7 +407,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
default=None,
description="KVTransfer parameters used for disaggregated serving.")
# doc: end-chat-completion-extra-params
# --8<-- [end:chat-completion-extra-params]
# Default sampling parameters for chat completion requests
_DEFAULT_SAMPLING_PARAMS: dict = {
......@@ -764,7 +764,7 @@ class CompletionRequest(OpenAIBaseModel):
top_p: Optional[float] = None
user: Optional[str] = None
# doc: begin-completion-sampling-params
# --8<-- [start:completion-sampling-params]
use_beam_search: bool = False
top_k: Optional[int] = None
min_p: Optional[float] = None
......@@ -779,9 +779,9 @@ class CompletionRequest(OpenAIBaseModel):
truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
allowed_token_ids: Optional[list[int]] = None
prompt_logprobs: Optional[int] = None
# doc: end-completion-sampling-params
# --8<-- [end:completion-sampling-params]
# doc: begin-completion-extra-params
# --8<-- [start:completion-extra-params]
add_special_tokens: bool = Field(
default=True,
description=(
......@@ -858,7 +858,7 @@ class CompletionRequest(OpenAIBaseModel):
default=None,
description="KVTransfer parameters used for disaggregated serving.")
# doc: end-completion-extra-params
# --8<-- [end:completion-extra-params]
# Default sampling parameters for completion requests
_DEFAULT_SAMPLING_PARAMS: dict = {
......@@ -1045,11 +1045,11 @@ class EmbeddingCompletionRequest(OpenAIBaseModel):
user: Optional[str] = None
truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None
# doc: begin-embedding-pooling-params
# --8<-- [start:embedding-pooling-params]
additional_data: Optional[Any] = None
# doc: end-embedding-pooling-params
# --8<-- [end:embedding-pooling-params]
# doc: begin-embedding-extra-params
# --8<-- [start:embedding-extra-params]
add_special_tokens: bool = Field(
default=True,
description=(
......@@ -1064,7 +1064,7 @@ class EmbeddingCompletionRequest(OpenAIBaseModel):
"if the served model does not use priority scheduling."),
)
# doc: end-embedding-extra-params
# --8<-- [end:embedding-extra-params]
def to_pooling_params(self):
return PoolingParams(dimensions=self.dimensions,
......@@ -1080,11 +1080,11 @@ class EmbeddingChatRequest(OpenAIBaseModel):
user: Optional[str] = None
truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None
# doc: begin-chat-embedding-pooling-params
# --8<-- [start:chat-embedding-pooling-params]
additional_data: Optional[Any] = None
# doc: end-chat-embedding-pooling-params
# --8<-- [end:chat-embedding-pooling-params]
# doc: begin-chat-embedding-extra-params
# --8<-- [start:chat-embedding-extra-params]
add_special_tokens: bool = Field(
default=False,
description=(
......@@ -1118,7 +1118,7 @@ class EmbeddingChatRequest(OpenAIBaseModel):
"default: 0). Any priority other than 0 will raise an error "
"if the served model does not use priority scheduling."),
)
# doc: end-chat-embedding-extra-params
# --8<-- [end:chat-embedding-extra-params]
@model_validator(mode="before")
@classmethod
......@@ -1147,11 +1147,11 @@ class ScoreRequest(OpenAIBaseModel):
text_2: Union[list[str], str]
truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None
# doc: begin-score-pooling-params
# --8<-- [start:score-pooling-params]
additional_data: Optional[Any] = None
# doc: end-score-pooling-params
# --8<-- [end:score-pooling-params]
# doc: begin-score-extra-params
# --8<-- [start:score-extra-params]
priority: int = Field(
default=0,
description=(
......@@ -1160,7 +1160,7 @@ class ScoreRequest(OpenAIBaseModel):
"if the served model does not use priority scheduling."),
)
# doc: end-score-extra-params
# --8<-- [end:score-extra-params]
def to_pooling_params(self):
return PoolingParams(additional_data=self.additional_data)
......@@ -1173,11 +1173,11 @@ class RerankRequest(OpenAIBaseModel):
top_n: int = Field(default_factory=lambda: 0)
truncate_prompt_tokens: Optional[Annotated[int, Field(ge=-1)]] = None
# doc: begin-rerank-pooling-params
# --8<-- [start:rerank-pooling-params]
additional_data: Optional[Any] = None
# doc: end-rerank-pooling-params
# --8<-- [end:rerank-pooling-params]
# doc: begin-rerank-extra-params
# --8<-- [start:rerank-extra-params]
priority: int = Field(
default=0,
description=(
......@@ -1186,7 +1186,7 @@ class RerankRequest(OpenAIBaseModel):
"if the served model does not use priority scheduling."),
)
# doc: end-rerank-extra-params
# --8<-- [end:rerank-extra-params]
def to_pooling_params(self):
return PoolingParams(additional_data=self.additional_data)
......@@ -1321,11 +1321,11 @@ class ClassificationRequest(OpenAIBaseModel):
truncate_prompt_tokens: Optional[int] = None
user: Optional[str] = None
# doc: begin-classification-pooling-params
# --8<-- [start:classification-pooling-params]
additional_data: Optional[Any] = None
# doc: end-classification-pooling-params
# --8<-- [end:classification-pooling-params]
# doc: begin-classification-extra-params
# --8<-- [start:classification-extra-params]
priority: int = Field(
default=0,
description=(
......@@ -1334,7 +1334,7 @@ class ClassificationRequest(OpenAIBaseModel):
"if the served model does not use priority scheduling."),
)
# doc: end-classification-extra-params
# --8<-- [end:classification-extra-params]
def to_pooling_params(self):
return PoolingParams(additional_data=self.additional_data)
......@@ -1698,7 +1698,7 @@ class TranscriptionRequest(OpenAIBaseModel):
timestamps incurs additional latency.
"""
# doc: begin-transcription-extra-params
# --8<-- [start:transcription-extra-params]
stream: Optional[bool] = False
"""Custom field not present in the original OpenAI definition. When set,
it will enable output to be streamed in a similar fashion as the Chat
......@@ -1707,9 +1707,9 @@ class TranscriptionRequest(OpenAIBaseModel):
# Flattened stream option to simplify form data.
stream_include_usage: Optional[bool] = False
stream_continuous_usage_stats: Optional[bool] = False
# doc: end-transcription-extra-params
# --8<-- [end:transcription-extra-params]
# doc: begin-transcription-sampling-params
# --8<-- [start:transcription-sampling-params]
temperature: float = Field(default=0.0)
"""The sampling temperature, between 0 and 1.
......@@ -1743,7 +1743,7 @@ class TranscriptionRequest(OpenAIBaseModel):
presence_penalty: Optional[float] = 0.0
"""The presence penalty to use for sampling."""
# doc: end-transcription-sampling-params
# --8<-- [end:transcription-sampling-params]
# Default sampling parameters for transcription requests.
_DEFAULT_SAMPLING_PARAMS: dict = {
......
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