Migrate docs from Sphinx to MkDocs (#18145)
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
5
docs/models/extensions/fastsafetensor.md
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docs/models/extensions/fastsafetensor.md
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Loading Model weights with fastsafetensors
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===================================================================
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Using fastsafetensors library enables loading model weights to GPU memory by leveraging GPU direct storage. See [their GitHub repository](https://github.com/foundation-model-stack/fastsafetensors) for more details.
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For enabling this feature, set the environment variable ``USE_FASTSAFETENSOR`` to ``true``
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78
docs/models/extensions/runai_model_streamer.md
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docs/models/extensions/runai_model_streamer.md
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---
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title: Loading models with Run:ai Model Streamer
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---
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[](){ #runai-model-streamer }
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Run:ai Model Streamer is a library to read tensors in concurrency, while streaming it to GPU memory.
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Further reading can be found in [Run:ai Model Streamer Documentation](https://github.com/run-ai/runai-model-streamer/blob/master/docs/README.md).
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vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer.
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You first need to install vLLM RunAI optional dependency:
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```console
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pip3 install vllm[runai]
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```
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To run it as an OpenAI-compatible server, add the `--load-format runai_streamer` flag:
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```console
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vllm serve /home/meta-llama/Llama-3.2-3B-Instruct --load-format runai_streamer
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```
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To run model from AWS S3 object store run:
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```console
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vllm serve s3://core-llm/Llama-3-8b --load-format runai_streamer
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```
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To run model from a S3 compatible object store run:
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```console
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RUNAI_STREAMER_S3_USE_VIRTUAL_ADDRESSING=0 AWS_EC2_METADATA_DISABLED=true AWS_ENDPOINT_URL=https://storage.googleapis.com vllm serve s3://core-llm/Llama-3-8b --load-format runai_streamer
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```
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## Tunable parameters
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You can tune parameters using `--model-loader-extra-config`:
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You can tune `concurrency` that controls the level of concurrency and number of OS threads reading tensors from the file to the CPU buffer.
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For reading from S3, it will be the number of client instances the host is opening to the S3 server.
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```console
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vllm serve /home/meta-llama/Llama-3.2-3B-Instruct --load-format runai_streamer --model-loader-extra-config '{"concurrency":16}'
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```
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You can control the size of the CPU Memory buffer to which tensors are read from the file, and limit this size.
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You can read further about CPU buffer memory limiting [here](https://github.com/run-ai/runai-model-streamer/blob/master/docs/src/env-vars.md#runai_streamer_memory_limit).
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```console
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vllm serve /home/meta-llama/Llama-3.2-3B-Instruct --load-format runai_streamer --model-loader-extra-config '{"memory_limit":5368709120}'
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```
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!!! note
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For further instructions about tunable parameters and additional parameters configurable through environment variables, read the [Environment Variables Documentation](https://github.com/run-ai/runai-model-streamer/blob/master/docs/src/env-vars.md).
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## Sharded Model Loading
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vLLM also supports loading sharded models using Run:ai Model Streamer. This is particularly useful for large models that are split across multiple files. To use this feature, use the `--load-format runai_streamer_sharded` flag:
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```console
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vllm serve /path/to/sharded/model --load-format runai_streamer_sharded
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```
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The sharded loader expects model files to follow the same naming pattern as the regular sharded state loader: `model-rank-{rank}-part-{part}.safetensors`. You can customize this pattern using the `pattern` parameter in `--model-loader-extra-config`:
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```console
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vllm serve /path/to/sharded/model --load-format runai_streamer_sharded --model-loader-extra-config '{"pattern":"custom-model-rank-{rank}-part-{part}.safetensors"}'
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```
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To create sharded model files, you can use the script provided in <gh-file:examples/offline_inference/save_sharded_state.py>. This script demonstrates how to save a model in the sharded format that is compatible with the Run:ai Model Streamer sharded loader.
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The sharded loader supports all the same tunable parameters as the regular Run:ai Model Streamer, including `concurrency` and `memory_limit`. These can be configured in the same way:
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```console
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vllm serve /path/to/sharded/model --load-format runai_streamer_sharded --model-loader-extra-config '{"concurrency":16, "memory_limit":5368709120}'
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```
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!!! note
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The sharded loader is particularly efficient for tensor or pipeline parallel models where each worker only needs to read its own shard rather than the entire checkpoint.
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16
docs/models/extensions/tensorizer.md
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docs/models/extensions/tensorizer.md
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---
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title: Loading models with CoreWeave's Tensorizer
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---
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[](){ #tensorizer }
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vLLM supports loading models with [CoreWeave's Tensorizer](https://docs.coreweave.com/coreweave-machine-learning-and-ai/inference/tensorizer).
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vLLM model tensors that have been serialized to disk, an HTTP/HTTPS endpoint, or S3 endpoint can be deserialized
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at runtime extremely quickly directly to the GPU, resulting in significantly
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shorter Pod startup times and CPU memory usage. Tensor encryption is also supported.
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For more information on CoreWeave's Tensorizer, please refer to
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[CoreWeave's Tensorizer documentation](https://github.com/coreweave/tensorizer). For more information on serializing a vLLM model, as well a general usage guide to using Tensorizer with vLLM, see
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the [vLLM example script](https://docs.vllm.ai/en/latest/getting_started/examples/tensorize_vllm_model.html).
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!!! note
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Note that to use this feature you will need to install `tensorizer` by running `pip install vllm[tensorizer]`.
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138
docs/models/generative_models.md
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docs/models/generative_models.md
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---
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title: Generative Models
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---
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[](){ #generative-models }
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vLLM provides first-class support for generative models, which covers most of LLMs.
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In vLLM, generative models implement the [VllmModelForTextGeneration][vllm.model_executor.models.VllmModelForTextGeneration] interface.
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Based on the final hidden states of the input, these models output log probabilities of the tokens to generate,
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which are then passed through [Sampler][vllm.model_executor.layers.Sampler] to obtain the final text.
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For generative models, the only supported `--task` option is `"generate"`.
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Usually, this is automatically inferred so you don't have to specify it.
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## Offline Inference
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The [LLM][vllm.LLM] class provides various methods for offline inference.
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See [configuration][configuration] for a list of options when initializing the model.
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### `LLM.generate`
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The [generate][vllm.LLM.generate] method is available to all generative models in vLLM.
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It is similar to [its counterpart in HF Transformers](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.generate),
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except that tokenization and detokenization are also performed automatically.
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```python
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from vllm import LLM
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llm = LLM(model="facebook/opt-125m")
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outputs = llm.generate("Hello, my name is")
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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```
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You can optionally control the language generation by passing [SamplingParams][vllm.SamplingParams].
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For example, you can use greedy sampling by setting `temperature=0`:
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```python
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from vllm import LLM, SamplingParams
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llm = LLM(model="facebook/opt-125m")
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params = SamplingParams(temperature=0)
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outputs = llm.generate("Hello, my name is", params)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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```
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!!! warning
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By default, vLLM will use sampling parameters recommended by model creator by applying the `generation_config.json` from the huggingface model repository if it exists. In most cases, this will provide you with the best results by default if [SamplingParams][vllm.SamplingParams] is not specified.
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However, if vLLM's default sampling parameters are preferred, please pass `generation_config="vllm"` when creating the [LLM][vllm.LLM] instance.
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A code example can be found here: <gh-file:examples/offline_inference/basic/basic.py>
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### `LLM.beam_search`
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The [beam_search][vllm.LLM.beam_search] method implements [beam search](https://huggingface.co/docs/transformers/en/generation_strategies#beam-search) on top of [generate][vllm.LLM.generate].
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For example, to search using 5 beams and output at most 50 tokens:
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```python
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from vllm import LLM
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from vllm.sampling_params import BeamSearchParams
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llm = LLM(model="facebook/opt-125m")
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params = BeamSearchParams(beam_width=5, max_tokens=50)
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outputs = llm.beam_search([{"prompt": "Hello, my name is "}], params)
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for output in outputs:
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generated_text = output.sequences[0].text
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print(f"Generated text: {generated_text!r}")
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```
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### `LLM.chat`
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The [chat][vllm.LLM.chat] method implements chat functionality on top of [generate][vllm.LLM.generate].
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In particular, it accepts input similar to [OpenAI Chat Completions API](https://platform.openai.com/docs/api-reference/chat)
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and automatically applies the model's [chat template](https://huggingface.co/docs/transformers/en/chat_templating) to format the prompt.
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!!! warning
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In general, only instruction-tuned models have a chat template.
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Base models may perform poorly as they are not trained to respond to the chat conversation.
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```python
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from vllm import LLM
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llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")
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conversation = [
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{
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"role": "system",
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"content": "You are a helpful assistant"
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},
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{
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"role": "user",
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"content": "Hello"
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},
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{
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"role": "assistant",
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"content": "Hello! How can I assist you today?"
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},
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{
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"role": "user",
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"content": "Write an essay about the importance of higher education.",
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},
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]
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outputs = llm.chat(conversation)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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```
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A code example can be found here: <gh-file:examples/offline_inference/basic/chat.py>
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If the model doesn't have a chat template or you want to specify another one,
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you can explicitly pass a chat template:
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```python
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from vllm.entrypoints.chat_utils import load_chat_template
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# You can find a list of existing chat templates under `examples/`
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custom_template = load_chat_template(chat_template="<path_to_template>")
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print("Loaded chat template:", custom_template)
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outputs = llm.chat(conversation, chat_template=custom_template)
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```
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## Online Serving
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Our [OpenAI-Compatible Server][openai-compatible-server] provides endpoints that correspond to the offline APIs:
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- [Completions API][completions-api] is similar to `LLM.generate` but only accepts text.
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- [Chat API][chat-api] is similar to `LLM.chat`, accepting both text and [multi-modal inputs][multimodal-inputs] for models with a chat template.
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193
docs/models/pooling_models.md
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docs/models/pooling_models.md
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---
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title: Pooling Models
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---
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[](){ #pooling-models }
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vLLM also supports pooling models, including embedding, reranking and reward models.
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In vLLM, pooling models implement the [VllmModelForPooling][vllm.model_executor.models.VllmModelForPooling] interface.
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These models use a [Pooler][vllm.model_executor.layers.Pooler] to extract the final hidden states of the input
|
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before returning them.
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!!! note
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We currently support pooling models primarily as a matter of convenience.
|
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As shown in the [Compatibility Matrix][compatibility-matrix], most vLLM features are not applicable to
|
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pooling models as they only work on the generation or decode stage, so performance may not improve as much.
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For pooling models, we support the following `--task` options.
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The selected option sets the default pooler used to extract the final hidden states:
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| Task | Pooling Type | Normalization | Softmax |
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|---------------------------------|----------------|-----------------|-----------|
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| Embedding (`embed`) | `LAST` | ✅︎ | ❌ |
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| Classification (`classify`) | `LAST` | ❌ | ✅︎ |
|
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| Sentence Pair Scoring (`score`) | \* | \* | \* |
|
||||
|
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\*The default pooler is always defined by the model.
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!!! note
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If the model's implementation in vLLM defines its own pooler, the default pooler is set to that instead of the one specified in this table.
|
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|
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When loading [Sentence Transformers](https://huggingface.co/sentence-transformers) models,
|
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we attempt to override the default pooler based on its Sentence Transformers configuration file (`modules.json`).
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!!! tip
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You can customize the model's pooling method via the `--override-pooler-config` option,
|
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which takes priority over both the model's and Sentence Transformers's defaults.
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|
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## Offline Inference
|
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|
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The [LLM][vllm.LLM] class provides various methods for offline inference.
|
||||
See [configuration][configuration] for a list of options when initializing the model.
|
||||
|
||||
### `LLM.encode`
|
||||
|
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The [encode][vllm.LLM.encode] method is available to all pooling models in vLLM.
|
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It returns the extracted hidden states directly, which is useful for reward models.
|
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|
||||
```python
|
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from vllm import LLM
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|
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llm = LLM(model="Qwen/Qwen2.5-Math-RM-72B", task="reward")
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(output,) = llm.encode("Hello, my name is")
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data = output.outputs.data
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print(f"Data: {data!r}")
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```
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### `LLM.embed`
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The [embed][vllm.LLM.embed] method outputs an embedding vector for each prompt.
|
||||
It is primarily designed for embedding models.
|
||||
|
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```python
|
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from vllm import LLM
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llm = LLM(model="intfloat/e5-mistral-7b-instruct", task="embed")
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(output,) = llm.embed("Hello, my name is")
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embeds = output.outputs.embedding
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print(f"Embeddings: {embeds!r} (size={len(embeds)})")
|
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```
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A code example can be found here: <gh-file:examples/offline_inference/basic/embed.py>
|
||||
|
||||
### `LLM.classify`
|
||||
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||||
The [classify][vllm.LLM.classify] method outputs a probability vector for each prompt.
|
||||
It is primarily designed for classification models.
|
||||
|
||||
```python
|
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from vllm import LLM
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||||
llm = LLM(model="jason9693/Qwen2.5-1.5B-apeach", task="classify")
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(output,) = llm.classify("Hello, my name is")
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probs = output.outputs.probs
|
||||
print(f"Class Probabilities: {probs!r} (size={len(probs)})")
|
||||
```
|
||||
|
||||
A code example can be found here: <gh-file:examples/offline_inference/basic/classify.py>
|
||||
|
||||
### `LLM.score`
|
||||
|
||||
The [score][vllm.LLM.score] method outputs similarity scores between sentence pairs.
|
||||
It is designed for embedding models and cross encoder models. Embedding models use cosine similarity, and [cross-encoder models](https://www.sbert.net/examples/applications/cross-encoder/README.html) serve as rerankers between candidate query-document pairs in RAG systems.
|
||||
|
||||
!!! note
|
||||
vLLM can only perform the model inference component (e.g. embedding, reranking) of RAG.
|
||||
To handle RAG at a higher level, you should use integration frameworks such as [LangChain](https://github.com/langchain-ai/langchain).
|
||||
|
||||
```python
|
||||
from vllm import LLM
|
||||
|
||||
llm = LLM(model="BAAI/bge-reranker-v2-m3", task="score")
|
||||
(output,) = llm.score("What is the capital of France?",
|
||||
"The capital of Brazil is Brasilia.")
|
||||
|
||||
score = output.outputs.score
|
||||
print(f"Score: {score}")
|
||||
```
|
||||
|
||||
A code example can be found here: <gh-file:examples/offline_inference/basic/score.py>
|
||||
|
||||
## Online Serving
|
||||
|
||||
Our [OpenAI-Compatible Server][openai-compatible-server] provides endpoints that correspond to the offline APIs:
|
||||
|
||||
- [Pooling API][pooling-api] is similar to `LLM.encode`, being applicable to all types of pooling models.
|
||||
- [Embeddings API][embeddings-api] is similar to `LLM.embed`, accepting both text and [multi-modal inputs][multimodal-inputs] for embedding models.
|
||||
- [Classification API][classification-api] is similar to `LLM.classify` and is applicable to sequence classification models.
|
||||
- [Score API][score-api] is similar to `LLM.score` for cross-encoder models.
|
||||
|
||||
## Matryoshka Embeddings
|
||||
|
||||
[Matryoshka Embeddings](https://sbert.net/examples/sentence_transformer/training/matryoshka/README.html#matryoshka-embeddings) or [Matryoshka Representation Learning (MRL)](https://arxiv.org/abs/2205.13147) is a technique used in training embedding models. It allows user to trade off between performance and cost.
|
||||
|
||||
!!! warning
|
||||
Not all embedding models are trained using Matryoshka Representation Learning. To avoid misuse of the `dimensions` parameter, vLLM returns an error for requests that attempt to change the output dimension of models that do not support Matryoshka Embeddings.
|
||||
|
||||
For example, setting `dimensions` parameter while using the `BAAI/bge-m3` model will result in the following error.
|
||||
|
||||
```json
|
||||
{"object":"error","message":"Model \"BAAI/bge-m3\" does not support matryoshka representation, changing output dimensions will lead to poor results.","type":"BadRequestError","param":null,"code":400}
|
||||
```
|
||||
|
||||
### Manually enable Matryoshka Embeddings
|
||||
|
||||
There is currently no official interface for specifying support for Matryoshka Embeddings. In vLLM, if `is_matryoshka` is `True` in `config.json,` it is allowed to change the output to arbitrary dimensions. Using `matryoshka_dimensions` can control the allowed output dimensions.
|
||||
|
||||
For models that support Matryoshka Embeddings but not recognized by vLLM, please manually override the config using `hf_overrides={"is_matryoshka": True}`, `hf_overrides={"matryoshka_dimensions": [<allowed output dimensions>]}` (offline) or `--hf_overrides '{"is_matryoshka": true}'`, `--hf_overrides '{"matryoshka_dimensions": [<allowed output dimensions>]}'`(online).
|
||||
|
||||
Here is an example to serve a model with Matryoshka Embeddings enabled.
|
||||
|
||||
```text
|
||||
vllm serve Snowflake/snowflake-arctic-embed-m-v1.5 --hf_overrides '{"matryoshka_dimensions":[256]}'
|
||||
```
|
||||
|
||||
### Offline Inference
|
||||
|
||||
You can change the output dimensions of embedding models that support Matryoshka Embeddings by using the dimensions parameter in [PoolingParams][vllm.PoolingParams].
|
||||
|
||||
```python
|
||||
from vllm import LLM, PoolingParams
|
||||
|
||||
model = LLM(model="jinaai/jina-embeddings-v3",
|
||||
task="embed",
|
||||
trust_remote_code=True)
|
||||
outputs = model.embed(["Follow the white rabbit."],
|
||||
pooling_params=PoolingParams(dimensions=32))
|
||||
print(outputs[0].outputs)
|
||||
```
|
||||
|
||||
A code example can be found here: <gh-file:examples/offline_inference/embed_matryoshka_fy.py>
|
||||
|
||||
### Online Inference
|
||||
|
||||
Use the following command to start vllm server.
|
||||
|
||||
```text
|
||||
vllm serve jinaai/jina-embeddings-v3 --trust-remote-code
|
||||
```
|
||||
|
||||
You can change the output dimensions of embedding models that support Matryoshka Embeddings by using the dimensions parameter.
|
||||
|
||||
```text
|
||||
curl http://127.0.0.1:8000/v1/embeddings \
|
||||
-H 'accept: application/json' \
|
||||
-H 'Content-Type: application/json' \
|
||||
-d '{
|
||||
"input": "Follow the white rabbit.",
|
||||
"model": "jinaai/jina-embeddings-v3",
|
||||
"encoding_format": "float",
|
||||
"dimensions": 32
|
||||
}'
|
||||
```
|
||||
|
||||
Expected output:
|
||||
|
||||
```json
|
||||
{"id":"embd-5c21fc9a5c9d4384a1b021daccaf9f64","object":"list","created":1745476417,"model":"jinaai/jina-embeddings-v3","data":[{"index":0,"object":"embedding","embedding":[-0.3828125,-0.1357421875,0.03759765625,0.125,0.21875,0.09521484375,-0.003662109375,0.1591796875,-0.130859375,-0.0869140625,-0.1982421875,0.1689453125,-0.220703125,0.1728515625,-0.2275390625,-0.0712890625,-0.162109375,-0.283203125,-0.055419921875,-0.0693359375,0.031982421875,-0.04052734375,-0.2734375,0.1826171875,-0.091796875,0.220703125,0.37890625,-0.0888671875,-0.12890625,-0.021484375,-0.0091552734375,0.23046875]}],"usage":{"prompt_tokens":8,"total_tokens":8,"completion_tokens":0,"prompt_tokens_details":null}}
|
||||
```
|
||||
|
||||
A openai client example can be found here: <gh-file:examples/online_serving/openai_embedding_matryoshka_fy.py>
|
||||
690
docs/models/supported_models.md
Normal file
690
docs/models/supported_models.md
Normal file
@@ -0,0 +1,690 @@
|
||||
---
|
||||
title: 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 [supported-text-models][supported-text-models] and [supported-mm-models][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 [offline-inference][offline-inference] or `--model-impl transformers` for the [openai-compatible-server][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 [writing-custom-models][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 [offline-inference][offline-inference] or `--trust-remote-code` for the [openai-compatible-server][openai-compatible-server].
|
||||
- in a local directory, simply pass directory path to `model=<MODEL_DIR>` for [offline-inference][offline-inference] or `vllm serve <MODEL_DIR>` for the [openai-compatible-server][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`.
|
||||
|
||||
```python title="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:
|
||||
|
||||
```python title="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`.
|
||||
|
||||
| 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. | | |
|
||||
|
||||
!!! 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.
|
||||
|
||||
!!! warning
|
||||
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`.
|
||||
|
||||
| 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. | | |
|
||||
|
||||
!!! 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
|
||||
[as_embedding_model][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`.
|
||||
|
||||
| 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. | ✅︎ | ✅︎ |
|
||||
|
||||
If your model is not in the above list, we will try to automatically convert the model using
|
||||
[as_reward_model][vllm.model_executor.models.adapters.as_reward_model]. By default, we return the hidden states of each token directly.
|
||||
|
||||
!!! warning
|
||||
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`.
|
||||
|
||||
| Architecture | Models | Example HF Models | [LoRA][lora-adapter] | [PP][distributed-serving] |
|
||||
|----------------------------------|----------|----------------------------------------|------------------------|-----------------------------|
|
||||
| `JambaForSequenceClassification` | Jamba | `ai21labs/Jamba-tiny-reward-dev`, etc. | ✅︎ | ✅︎ |
|
||||
|
||||
If your model is not in the above list, we will try to automatically convert the model using
|
||||
[as_classification_model][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`.
|
||||
|
||||
| Architecture | Models | Example HF Models |
|
||||
|---------------------------------------|-------------------|----------------------------------------------|
|
||||
| `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. |
|
||||
|
||||
[](){ #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.
|
||||
|
||||
!!! warning
|
||||
**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`.
|
||||
|
||||
| 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` | ✅︎ | ✅︎ | |
|
||||
|
||||
<sup>^</sup> You need to set the architecture name via `--hf-overrides` to match the one in vLLM.
|
||||
• For example, to use DeepSeek-VL2 series models:
|
||||
`--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.
|
||||
|
||||
!!! warning
|
||||
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.
|
||||
|
||||
| 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` | 🚧 | ✅︎ |
|
||||
|
||||
#### Transcription
|
||||
|
||||
Specified using `--task transcription`.
|
||||
|
||||
Speech2Text models trained specifically for Automatic Speech Recognition.
|
||||
|
||||
| Architecture | Models | Example HF Models | [LoRA][lora-adapter] | [PP][distributed-serving] |
|
||||
|----------------|----------|---------------------|------------------------|-----------------------------|
|
||||
|
||||
---
|
||||
|
||||
## 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.
|
||||
Reference in New Issue
Block a user