Vllm multiple models examples. Hosting a vLLM Server.

Vllm multiple models examples 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. By extracting hidden states, vLLM can automatically convert text generation models like Llama-3-8B, Mistral-7B-Instruct-v0. Here’s how the process works: Draft Model: A smaller, more efficient model proposes tokens one by one. 5 """ 6 from argparse import Namespace 7 from typing import List, NamedTuple, Optional 8 9 from PIL. The first and the best multi-agent framework. 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. 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. 4 5 For most models, 146 run_encode (args. vLLM provides experimental support for multi-modal models through the vllm. However, for models that include new operators (e. top of quantized models. # TODO: Add more instructions on how to do that once embeddings is in. image import ImageAsset 3 4 5 def run_llava (): 6 llm = LLM (model = "llava-hf/llava-1. (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. Does vLLM support th Examples. API Client. Aquila, Aquila2. vLLM provides experimental support for multi-modal models through the vllm. LoRA. For example, tensor parallelism needs to shard the model weights, and quantization needs to quantize the model weights. For example, if you have 4 GPUs in a single node, you can set the tensor parallel size to 4. 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. 4 5 For most models, 71 72 audio_count = args. Supported Models; Adding a New Model; Enabling Multimodal Inputs; Engine Arguments; Using LoRA adapters; Using VLMs; Speculative decoding in vLLM; Performance and Tuning; Quantization. PP. 6-mistral-7b-hf", max_model_len = 4096) 11 12 prompt = "[INST] <image> \n What is shown in this image? 5. A: You can try e5-mistral-7b-instruct and BAAI/bge-base-en-v1. 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. The complexity of adding a new model depends heavily on the model’s architecture. The format of the model config to load. OpenAI Compatible Server; Deploying with Docker; Distributed Inference and Serving; Production Metrics; Environment Variables; Usage Stats Collection; Examples# Scripts. py. 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. The other way is to change the model weights during the model initialization. You can register input I use Llama 3 for the examples with adapters for function calling and chat. LoRA Adapters; This page teaches you how to pass multi-modal inputs to multi-modal models in vLLM. 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 The complexity of adding a new model depends heavily on the model’s architecture. To do this, substitute your model’s linear and embedding layers with their tensor-parallel versions. 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. 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. org - camel-ai/camel previous. Image import 341 342 343 model_example_map = {344 1 from vllm import LLM, SamplingParams 2 from vllm. Models. Debugging Tips. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; INT8 W8A8 The complexity of adding a new model depends heavily on the model’s architecture. By the vLLM Team Note that, as an inference engine, vLLM does not introduce new models. LLM Engine Example. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; INT8 W8A8 Tensorize vLLM Model; Serving. prompt: The prompt should follow the format that is documented on HuggingFace. PromptInputs. 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. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; GGUF; INT8 A code example can be found in examples/offline_inference_vision_language. 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. OpenAI Chat Completions API with vLLM# vLLM is designed to also support the OpenAI Chat Completions API. See the Tensorize vLLM Model script in the Examples section for more information. It uses the OpenAI Chat Completions API, which easily integrates with other LLM tools. 4 5 Requires HuggingFace credentials for access to Llama2. Sometimes, there is a need to process inputs at the LLMEngine level before they are passed to the model executor. If a model supports more than one task, you can set the task via the --task argument. 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; 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. You can start multiple vLLM server replicas and use a Loading Models with CoreWeave’s Tensorizer; Models. This has complicated their interface far beyond “text-in, text-out”. Image#. 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. multi_modal_data: This is a dictionary that follows the schema defined in vllm. Below is a detailed guide on how to utilize LiteLLM with VLLM models effectively. 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. Speculative decoding transforms this process by allowing multiple tokens to be proposed and verified in one forward pass. assets. 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. Llava Next Example# Source vllm-project/vllm. 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. The task to use the model for. camel-ai. vLLM can serve multiple adapters simultaneously without noticeable delays, allowing the seamless use of multiple LoRA adapters. . You can pass a single image to the 'image' field The model will be inferred based on the model served on the vLLM server. Ray serve's vLLM example does not work with multiple models and tensor parallelism. In the documentation, I can see that multiple models are served using modes like Leader mode and Orchestrator mode. 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. The example also sets up multi-GPU or multi-HPU serving with Ray Serve using placement groups. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; FP8; FP8 different LoRA adapters (using the same model for demo purposes). By the vLLM Team next. Examples. 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. 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. 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. In theory, vLLM supports bitsandbytes and loading adapters on top of quantized models. https://www. To enable distributed inference the following additions need to made to the model-config. 0, 273 max_tokens = 128, 274 stop_token_ids = req_data. Name or path of the huggingface tokenizer to use. 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. 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. ') 256 parser. When the model only supports one task, “auto” can be used to select it; otherwise, you must specify explicitly which task to use. 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. PromptType:. 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. One way is to change the model weights after the model is initialized. 5; more are listed here. 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. stop_token_ids) The complete code of the examples can be found on examples/openai_chat_completion_structured_outputs. Loading Models with CoreWeave’s Tensorizer; Models. Note that, as an inference engine, vLLM does not introduce new models. To get started with LiteLLM and VLLM, you need to set up your environment and make a simple API call. 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. “bitsandbytes” will load the weights using bitsandbytes quantization. Default: “auto”--tokenizer. This is useful for tasks that require context or more detailed explanations. Each vLLM instance only supports one task, even if the same model can be used for multiple tasks. 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. Default: “auto”--config-format. 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. The tensor parallel size is the number of GPUs you want to use. Multi-modal inputs can be passed alongside text and token prompts to supported models via the multi_modal_data field in vllm. 3 into embedding models, but they are expected be inferior to models that are specifically trained on embedding tasks. 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. OpenAI’s API has emerged as a standard for that interface, and it is supported by open source LLM serving frameworks like vLLM. Supported Hardware for Quantization Kernels; AutoAWQ I would like to use techniques such as Multi-instance Support supported by the tensorrt-llm backend. This makes it ideal for deploying models in production Supported Models# vLLM supports generative and pooling models across various tasks. Experimental Automatic Parsing (OpenAI API)# This section covers the OpenAI beta wrapper over the client. Hosting a vLLM Server. 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. A more detailed client example can be found here. completions. stop_token_ids) 1 from vllm import LLM 2 from vllm. 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. multimodal package. Quick Start. Click here to view docs for the latest stable release. OpenAI previous. Currently, vLLM only has built-in support for image data. 1 """ 2 This example shows how to use vLLM for running offline inference 3 with the correct prompt format on vision language 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. Multi-Modality#. To enable multi-modal support for a model, please follow the guide for adding a new multimodal model. 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. To enable multiple multi-modal items per text prompt, you have to . Example HF Models. 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. OpenAI Compatible Server; Deploying with Docker 1 """ 2 This example shows how to use the multi-LoRA functionality 3 for offline inference. There are two possible ways to implement this feature. 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. 🐫 CAMEL: Finding the Scaling Law of Agents. 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. Possible choices: auto, hf, mistral. Multi-image input# Multi-image input is only supported for a subset of VLMs, as shown here. These adapters need to be loaded on top of the LLM for inference. 11 12 1 """An example showing how to use vLLM to serve multimodal models 2 and run online inference with OpenAI client. (Optional) Register input processor#. Supported Models; Generative Models; Pooling Models; Adding a New Model; Enabling Multimodal Inputs; Usage. Right now vLLM is a serving engine for a single model. AquilaForCausalLM. 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. To input multi-modal data, follow this schema in vllm. multimodal. 5. pil_image 11 12 outputs = llm. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; INT8 W8A8 Check out the vLLM models directory for more examples. By the vLLM Team “tensorizer” will load the weights using tensorizer from CoreWeave. However, this support has been added recently and is not fully optimized or applied to all the models supported by vLLM. 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. 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. BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc. 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. 0, 208 max_tokens = 128, 209 stop_token_ids = req_data. To vLLM provides experimental support for Vision Language Models (VLMs), allowing users to deploy multiple models efficiently. 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. 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. 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. vLLM chooses the Offline Inference#. next. Use a Model Hosted Locally The tensor parallel size is the number of GPUs you want to use. 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. To create an OpenAI-Compatible Server via vLLM you can follow the steps in the Quickstart section of their documentation. 3. PromptType. The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM. 5-7b-hf") 7 8 prompt = "USER: <image> \n What is the content of this image? \n ASSISTANT:" 9 10 image = ImageAsset ("stop_sign"). 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. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; INT8 W8A8 Loading Models with CoreWeave’s Tensorizer; Frequently Asked Questions; Models. create() method that provides richer integrations with Python specific Multi-Modality#. 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. MultiModalDataDict. 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 Check out the vLLM models directory for more examples. Therefore, all models supported by vLLM are third-party models in this regard. 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. inputs. 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. 10 # You may lower either to run this example on lower-end GPUs. 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. By the vLLM Team © Copyright 2024, vLLM Team. You are viewing the latest developer preview docs. model_name, args. , a new attention mechanism), the process can be a bit more complex. We are actively iterating on multi-modal support. For more configuration examples, take a look at the unit-tests. 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 each task, we list the model architectures that have been implemented in vLLM. You can register input Multi-Modality#. 4 5 For most models, the prompt format should follow corresponding examples 6 on HuggingFace model repository. g. chat. zricccc fbos lrzd ojuz nwsdrb cyrqnm sdxrhgs pvjd ixwwawi cndgzaah