Vllm multiple models examples Supported Hardware for Quantization Kernels; AutoAWQ; FP8; FP8 E5M2 KV Cache LLMs do more than just model language: they chat, they produce JSON and XML, they run code, and more. Note that, as an inference engine, vLLM does not introduce new models. create() method that provides richer integrations with Python specific Multi-Modality#. This makes it ideal for deploying models in production Supported Models# vLLM supports generative and pooling models across various tasks. We have the following levels of testing for models: Strict Consistency: We compare the output of the model with the output of the model in the HuggingFace Transformers library under greedy Loading Models with CoreWeave’s Tensorizer; Frequently Asked Questions; Models. Multi-Node Multi-GPU (tensor parallel plus pipeline parallel inference): If your model is too large to fit in a single node, you can use tensor parallel together with pipeline parallelism Note that, as an inference engine, vLLM does not introduce new models. To do this, substitute your model’s linear and embedding layers with their tensor-parallel versions. Alongside each architecture, we include some popular models that 1 """ 2 This example shows how to use vLLM for running offline inference with 3 multi-image input on vision language models, 158 llm, prompt, stop_token_ids, image_data, _ = model_example_map [model](159 question, image_urls) 160 if image_data is None: 161 image_data = Note that, as an inference engine, vLLM does not introduce new models. vLLM chooses the Allow user to specify multiple models to download when loading server Allow user to switch between models Allow user to load multiple models on the cluster (nice to have) +1, at the very least would be great to see an example. Does vLLM support th Examples. Models. inputs. Sometimes, there is a need to process inputs at the LLMEngine level before they are passed to the model executor. Examples. Use a Model Hosted Locally The tensor parallel size is the number of GPUs you want to use. In the documentation, I can see that multiple models are served using modes like Leader mode and Orchestrator mode. 4 5 For most models, 71 72 audio_count = args. This is often due to the fact that unlike implementations in HuggingFace Transformers, the reshaping and/or expansion of multi-modal embeddings needs to take place outside model’s forward() call. PromptType:. OpenAI Chat Completions API with vLLM# vLLM is designed to also support the OpenAI Chat Completions API. AquilaForCausalLM. You can register input I use Llama 3 for the examples with adapters for function calling and chat. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; GGUF; INT8 The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM. The tensor parallel size is the number of GPUs you want to use. Image import 341 342 343 model_example_map = {344 1 from vllm import LLM, SamplingParams 2 from vllm. ') 256 parser. OpenAI Compatible Server; Deploying with Docker; Deploying with Kubernetes 1 """ 2 This example shows how to use the multi-LoRA functionality 3 for offline inference. 1 """ 2 This example shows how to use vLLM for running offline inference 3 with the correct prompt format on vision language models. assets. You can pass a single image to the 'image' field The model will be inferred based on the model served on the vLLM server. Each vLLM instance only supports one task, even if the same model can be used for multiple tasks. 4 5 Requires HuggingFace credentials for access to Llama2. For more configuration examples, take a look at the unit-tests. vLLM provides experimental support for multi-modal models through the vllm. Debugging Tips. Since we also set `max_loras=1`, the expectation is that the requests with the second LoRA adapter will be ran after all requests with the Tensorize vLLM Model; Serving. OpenAI previous. Speculative decoding transforms this process by allowing multiple tokens to be proposed and verified in one forward pass. 7 """ 8 from transformers import AutoTokenizer 9 10 from vllm import LLM, SamplingParams 11 from vllm Loading Models with CoreWeave’s Tensorizer; Frequently Asked Questions; Models. It uses the OpenAI Chat Completions API, which easily integrates with other LLM tools. You are viewing the latest developer preview docs. Quick Start. However, for models that include new operators (e. If a model supports more than one task, you can set the task via the --task argument. We have the following levels of testing for models: Strict Consistency: We compare the output of the model with the output of the model in the HuggingFace Transformers library under greedy Note that, as an inference engine, vLLM does not introduce new models. To create an OpenAI-Compatible Server via vLLM you can follow the steps in the Quickstart section of their documentation. LoRA Adapters; Multimodal Inputs; Tool Calling; Structured Outputs; Speculative decoding; Compatibility Matrix; Performance and Tuning; Frequently Asked Questions; Engine Arguments vLLM supports generative and pooling models across various tasks. The chat interface is a more interactive way to communicate with the model, allowing back-and-forth exchanges that can be stored in the chat history. Supported Models; Adding a New Model; Enabling Multimodal Inputs; Engine Arguments; Using LoRA adapters; Using VLMs; Speculative decoding in vLLM; Performance and Tuning; Quantization. add_argument This example shows how to use vLLM for running offline inference with multi-image input on vision language models for text generation, using the chat template defined by the model. 0, 273 max_tokens = 128, 274 stop_token_ids = req_data. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; INT8 W8A8 Check out the vLLM models directory for more examples. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; GGUF; INT8 A code example can be found in examples/offline_inference_vision_language. 3 into embedding models, but they are expected be inferior to models that are specifically trained on embedding tasks. top of quantized models. See the Tensorize vLLM Model script in the Examples section for more information. 7 """ 8 from transformers import AutoTokenizer 9 10 from vllm import LLM, SamplingParams 11 from vllm 1 """ 2 This example shows how to use vLLM for running offline inference with 3 multi-image input on vision language models, 270 req_data = model_example_map [model](question, image_urls) 271 272 sampling_params = SamplingParams (temperature = 0. https://www. (Optional) Register input processor#. OpenAI’s API has emerged as a standard for that interface, and it is supported by open source LLM serving frameworks like vLLM. pil_image 11 12 outputs = llm. API Client; Aqlm Example; Gradio OpenAI Chatbot Webserver; Gradio Webserver; Llava Example; LLM Engine Example; MultiLoRA Inference; Offline Inference; Offline Inference Distributed; Offline Inference Neuron; Offline Inference With Prefix; OpenAI Chat Completion Client; OpenAI Completion Client; Tensorize vLLM Model; Serving. , a new attention mechanism), the process can be a bit more complex. PromptInputs. To vLLM provides experimental support for Vision Language Models (VLMs), allowing users to deploy multiple models efficiently. image import ImageAsset 3 4 5 def run_llava (): 6 llm = LLM (model = "llava-hf/llava-1. To enable multiple multi-modal items per text prompt, you have to . OpenAI Compatible Server; Deploying with Docker; Distributed Inference and Serving; Production Metrics; Environment Variables; Usage Stats Collection; Examples# Scripts. 6 """ 7 8 from typing import List, Optional, Tuple 9 10 from huggingface_hub import 1 """ 2 This example shows how to use vLLM for running offline inference with 3 multi-image input on vision language models, using the chat template defined 4 by the model. MultiModalDataDict. Llava Next Example# Source vllm-project/vllm. The process is considerably straightforward if the model shares a similar architecture with an existing model For more advanced features like multi-lora support with serve multiplexing, JSON mode function calling and further performance improvements, try LLM deployment solutions on Anyscale. Supported Hardware for Quantization Kernels; AutoAWQ I would like to use techniques such as Multi-instance Support supported by the tensorrt-llm backend. py. 5 """ 6 from argparse import Namespace 7 from typing import List, NamedTuple, Optional 8 9 from PIL. PromptType. Right now vLLM is a serving engine for a single model. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; INT8 W8A8 Tensorize vLLM Model; Serving. 3. Currently, vLLM only has built-in support for image data. g. When the model only supports one task, “auto” can be used to select it; otherwise, you must specify explicitly which task to use. stop_token_ids) 1 from vllm import LLM 2 from vllm. By the vLLM Team Note that, as an inference engine, vLLM does not introduce new models. In the following example, we instantiate a text generation model off of the Hugging Face model hub (jondurbin Note that, as an inference engine, vLLM does not introduce new models. 5 """ 6 from argparse import Namespace 7 from typing import List 8 9 from transformers import AutoProcessor, AutoTokenizer 10 11 from vllm import LLM, SamplingParams 12 from By default, vLLM models do not support multi-modal inputs. multimodal package. 4 5 For most models, 179 180 llm, prompt, stop_token_ids = model_example_map [model](question) This example shows how to use vLLM for running offline inference with the correct prompt format on vision language models for multimodal embedding. 6 """ 7 8 from typing import List, Optional, Tuple 9 10 from huggingface We define 2 22 different LoRA Loading Models with CoreWeave’s Tensorizer; Compatibility Matrix; Frequently Asked Questions; Models. Default: “auto”--tokenizer. We have the following levels of testing for models: Strict Consistency: We compare the output of the model with the output of the model in the HuggingFace Transformers library under greedy Once installed on a suitable Python environment, the vLLM API is simple enough to use. By the vLLM Team © Copyright 2024, vLLM Team. Possible choices: auto, hf, mistral. This section outlines how to run and serve these Explore the vllm multimodal example using Litellm, showcasing its capabilities in handling diverse data types effectively. 4 5 For most models, 146 run_encode (args. We have the following levels of testing for models: Strict Consistency: We compare the output of the model with the output of the model in the HuggingFace Transformers library under greedy LiteLLM provides seamless integration with VLLM models, allowing developers to leverage the capabilities of various language models effortlessly. Multi-Node Multi-GPU (tensor parallel plus pipeline parallel inference): If your model is too large to fit in a single node, you can use tensor parallel together with pipeline parallelism Loading Models with CoreWeave’s Tensorizer; Frequently Asked Questions; Models. LiteLLM provides seamless integration with VLLM models, allowing 1 """ 2 This example shows how to use vLLM for running offline inference with 3 multi-image input on vision language models for text generation, 4 using the chat template defined by the model. For each task, we list the model architectures that have been implemented in vLLM. Below is a detailed guide on how to utilize LiteLLM with VLLM models effectively. Multi-Modality#. The task to use the model for. By extracting hidden states, vLLM can automatically convert text generation models like Llama-3-8B, Mistral-7B-Instruct-v0. Multi-Node Multi-GPU (tensor parallel plus pipeline parallel inference): If your model is too large to fit in a single node, you can use tensor parallel together with pipeline parallelism The complexity of adding a new model depends heavily on the model’s architecture. To get started with LiteLLM and VLLM, you need to set up your environment and make a simple API call. The format of the model config to load. PP. completions. LLM Engine Example. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; INT8 W8A8 The complexity of adding a new model depends heavily on the model’s architecture. Click here to view docs for the latest stable release. 1 """ 2 This example shows how to use vLLM for running offline inference with 3 the correct prompt format on vision language models for multimodal embedding. 5; more are listed here. Default: “auto”--config-format. # TODO: Add more instructions on how to do that once embeddings is in. Supported Models; Generative Models; Pooling Models; Adding a New Model; Enabling Multimodal Inputs; Usage. A: You can try e5-mistral-7b-instruct and BAAI/bge-base-en-v1. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; INT8 W8A8 Loading Models with CoreWeave’s Tensorizer; Frequently Asked Questions; Models. By the vLLM Team “tensorizer” will load the weights using tensorizer from CoreWeave. You can register input The vLLM server is designed to support the OpenAI Chat API, allowing you to engage in dynamic conversations with the model. You can start multiple vLLM server replicas and use a Loading Models with CoreWeave’s Tensorizer; Models. Loading Models with CoreWeave’s Tensorizer; Frequently Asked Questions; Models. BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc. 10 # You may lower either to run this example on lower-end GPUs. API Client; Aqlm Example; Fuyu Example; Gradio OpenAI Chatbot Webserver; Gradio Webserver; Llava Example; Llava Next Example; LLM Engine Example; Lora With Quantization Inference; MultiLoRA Inference; Offline Inference; Offline Inference Arctic; Offline Inference Distributed; Offline Inference Embedding; Offline Inference previous. 6-mistral-7b-hf", max_model_len = 4096) 11 12 prompt = "[INST] <image> \n What is shown in this image? 5. Loading Models with CoreWeave’s Tensorizer; Models. 1 """ 2 This example shows how to use vLLM for running offline inference with 3 multi-image input on vision language models, 205 req_data = model_example_map [model](question, image_urls) 206 207 sampling_params = SamplingParams (temperature = 0. 1 - 405B - FP8 such as dynamic batching and memory-efficient model serving, vLLM ensures that even large models can be served with minimal resource overhead. To input multi-modal data, follow this schema in vllm. OpenAI Compatible Server; Deploying with Docker 1 """ 2 This example shows how to use the multi-LoRA functionality 3 for offline inference. API Client. We have the following levels of testing for models: Strict Consistency: We compare the output of the model with the output of the model in the HuggingFace Transformers library under greedy Check out the vLLM models directory for more examples. See this RFC for upcoming changes, and open an Seamless integration with popular HuggingFace models; High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more; Tensor parallelism support for distributed inference; Streaming outputs; This example shows how to use vLLM for running offline inference with multi-image input on vision language models for text generation, using the chat template defined by the model. (Optional) Implement tensor parallelism and quantization support# If your model is too large to fit into a single GPU, you can use tensor parallelism to manage it. 🐫 CAMEL: Finding the Scaling Law of Agents. prompt: The prompt should follow the format that is documented on HuggingFace. A more detailed client example can be found here. 5. I tried it last week and Multi-node & Multi-GPU inference with vLLM Multi-node & Multi-GPU inference with vLLM Table of contents Objective Llama 3. LoRA. The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM. multimodal. model_name, args. org - camel-ai/camel previous. Therefore, all models supported by vLLM are third-party models in this regard. However, this support has been added recently and is not fully optimized or applied to all the models supported by vLLM. Experimental Automatic Parsing (OpenAI API)# This section covers the OpenAI beta wrapper over the client. These adapters need to be loaded on top of the LLM for inference. Image#. One way is to change the model weights after the model is initialized. generate ({13 "prompt": prompt, 14 "multi_modal_data 1 """ 2 This example shows how to use vLLM for running offline inference 3 with the correct prompt format on vision language models. modality) 147 148 149 model_example_map = {150 "e5_v": run_e5_v, 151 "vlm2vec": run_vlm2vec, 152} For example, tensor parallelism needs to shard the model weights, and quantization needs to quantize the model weights. This is useful for tasks that require context or more detailed explanations. vLLM chooses the Offline Inference#. 3 4 Launch the vLLM server with the following command: 5 6 (254 description = 'Demo on using OpenAI client for online inference with ' 255 'multimodal language models served with vLLM. Hosting a vLLM Server. You can register input Multi-Modality#. Ray serve's vLLM example does not work with multiple models and tensor parallelism. vLLM can serve multiple adapters simultaneously without noticeable delays, allowing the seamless use of multiple LoRA adapters. 4 5 For most models, the prompt format should follow corresponding examples 6 on HuggingFace model repository. “bitsandbytes” will load the weights using bitsandbytes quantization. By the vLLM Team next. Aquila, Aquila2. multi_modal_data: This is a dictionary that follows the schema defined in vllm. To enable multi-modal support for a model, please follow the guide for adding a new multimodal model. 11 12 1 """An example showing how to use vLLM to serve multimodal models 2 and run online inference with OpenAI client. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; FP8; FP8 different LoRA adapters (using the same model for demo purposes). Example HF Models. chat. The first and the best multi-agent framework. Name or path of the huggingface tokenizer to use. For most models, the prompt format should follow corresponding examples For example, given a prompt, the model generates three tokens T1, T2, T3, each requiring a separate forward pass. We have the following levels of testing for models: Strict Consistency: We compare the output of the model with the output of the model in the HuggingFace Transformers library under greedy 1 """ 2 This example shows how to use vLLM for running offline inference 3 with the correct prompt format on vision language models. vLLM provides experimental support for multi-modal models through the vllm. We are actively iterating on multi-modal support. To enable distributed inference the following additions need to made to the model-config. LoRA Adapters; This page teaches you how to pass multi-modal inputs to multi-modal models in vLLM. In theory, vLLM supports bitsandbytes and loading adapters on top of quantized models. However, this support has been added 1 """ 2 This example shows how to use vLLM for running offline inference 3 with the correct prompt format on audio language models. For example, if you have 4 GPUs in a single node, you can set the tensor parallel size to 4. Image import 341 342 343 model_example_map = {344 Serve a Large Language Model with vLLM# This example runs a large language model with Ray Serve using vLLM, a popular open-source library for serving LLMs. 1 from io import BytesIO 2 3 import requests 4 from PIL import Image 5 6 from vllm import LLM, SamplingParams 7 8 9 def run_llava_next (): 10 llm = LLM (model = "llava-hf/llava-v1. This has complicated their interface far beyond “text-in, text-out”. Multi-modal inputs can be passed alongside text and token prompts to supported models via the multi_modal_data field in vllm. . The chat interface is a more dynamic, interactive way to communicate with the model, allowing back-and-forth exchanges that can be stored in the chat history. image import ImageAsset 3 4 5 def run_phi3v (): 6 model_path = "microsoft/Phi-3-vision-128k-instruct" 7 8 # Note: The default setting of max_num_seqs (256) and 9 # max_model_len (128k) for this model may cause OOM. Guides# Tensorize vLLM Model; Serving. We have the following levels of testing for models: Strict Consistency: We compare the output of the model with the output of the model in the HuggingFace Transformers library under greedy The complexity of adding a new model depends heavily on the model’s architecture. Currently, vLLM only has built-in support for With multiple model instances, the sever will dispatch the requests to different instances to reduce the overhead. yaml of the examples where 4 is the number of desired GPUs to use for the inference: # Supported Models# vLLM supports generative and pooling models across various tasks. API Client; Aqlm Example; Gradio OpenAI Chatbot Webserver; Gradio Webserver; Llava Example; LLM Engine Example; Lora With Quantization Inference; 1 """ 2 This example shows how to use vLLM for running offline inference with 3 multi-image input on vision language models for text generation, 4 using the chat template defined by the model. For example, tensor parallelism needs to shard the model weights, and quantization needs to quantize the model weights. 0, 208 max_tokens = 128, 209 stop_token_ids = req_data. 5-7b-hf") 7 8 prompt = "USER: <image> \n What is the content of this image? \n ASSISTANT:" 9 10 image = ImageAsset ("stop_sign"). next. The example also sets up multi-GPU or multi-HPU serving with Ray Serve using placement groups. The complexity of adding a new model depends heavily on the model’s architecture. Supported Hardware for Quantization Kernels; AutoAWQ; FP8; FP8 E5M2 KV Cache All examples can be easily distributed over multiple GPUs by enabling tensor parallelism in vLLM. camel-ai. Multi-image input# Multi-image input is only supported for a subset of VLMs, as shown here. stop_token_ids) The complete code of the examples can be found on examples/openai_chat_completion_structured_outputs. The other way is to change the model weights during the model initialization. Here’s how the process works: Draft Model: A smaller, more efficient model proposes tokens one by one. num_audios 73 llm, prompt, stop_token_ids = model_example_map [model](74 question_per_audio_count [audio_count] Loading Models with CoreWeave’s Tensorizer; Frequently Asked Questions; Models. There are two possible ways to implement this feature. tgy mitrd qxaowg zcd liwq eujcc rgvwzt oujbh isdr xpox