Vllm multiple models examples. Right now vLLM is a serving engine for a single model.
● Vllm multiple models examples You can register input The complexity of adding a new model depends heavily on the model’s architecture. 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. 6 """ 7 8 from typing import List, Optional, Tuple 9 10 from huggingface_hub import 1 from vllm import LLM, SamplingParams 2 from vllm. 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. 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. This makes it ideal for deploying models in production 🐫 CAMEL: Finding the Scaling Law of Agents. API Client. We are actively iterating on multi-modal support. This section outlines how to run and serve these Explore how Vllm handles multiple requests efficiently, enhancing performance and scalability in your applications. vLLM provides experimental support for multi-modal models through the vllm. The tensor parallel size is the number of GPUs you want to use. 3 model. 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. This only consists of text input by default, which may not be applicable to multi-modal models. The following example deploys the Mistral-7B-Instruct-v0. 5-7b-hf --chat-template template_llava. When the model only supports one task, “auto” can be used to select it; otherwise, you must specify explicitly which task to use. Image import 341 342 343 model_example_map = {344 All examples can be easily distributed over multiple GPUs by enabling tensor parallelism in vLLM. This includes: Running offline batched inference on datasets. Default: “auto”--tokenizer. 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. BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc. The first and the best multi-agent framework. 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. 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. By the vLLM Team Check out vllm/model_executor/models for more examples. Supported Models; Adding a New Model; Enabling Multimodal Inputs; Engine Arguments; Using LoRA adapters; Using VLMs; Speculative decoding in vLLM; Performance and Tuning; Quantization. However, for models that include new operators (e. multimodal package. 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 Multi-node & Multi-GPU inference with vLLM Multi-node & Multi-GPU inference with vLLM Table of contents Objective Llama 3. LiteLLM provides seamless integration with VLLM models, allowing developers to leverage the capabilities of various language models effortlessly. camel-ai. jinja 8 9 (multi-image inference with Phi-3. multi_modal_data: This is a dictionary that follows the schema defined in vllm. (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. (Optional) Register input processor#. Below is a detailed guide on how to utilize LiteLLM with VLLM models effectively. org - camel-ai/camel Supported Models# vLLM supports generative and pooling models across various tasks. To input multi-modal data, follow this schema in vllm. This is useful for tasks that require context or more detailed explanations. 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. API Client; Aqlm Example; Gradio OpenAI Chatbot Webserver; Gradio Webserver; Llava Example; LLM Engine Example; Lora With Quantization Inference; Tensorize vLLM Model; Serving. g. inputs. 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. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; INT8 W8A8 It's important to note that vLLM functions as an inference engine and does not introduce new models. PromptType. 1 """An example showing how to use vLLM to serve multimodal models 2 and run online inference with OpenAI client. You can start multiple vLLM server replicas and use a This page teaches you how to pass multi-modal inputs to multi-modal models in vLLM. The example also sets up multi-GPU or multi-HPU serving with Ray Serve using placement groups. Image import 341 342 343 model_example_map = {344 Tensorize vLLM Model; Serving. Multi-modal inputs can be passed alongside text and token prompts to supported models via the multi_modal_data field in vllm. previous. 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. assets. Supported Hardware for Quantization Kernels; AutoAWQ; FP8; FP8 E5M2 KV Cache The vLLM server is designed to support the OpenAI Chat API, allowing you to engage in dynamic conversations with the model. Example HF Models. 6-mistral-7b-hf", max_model_len = 4096) 11 12 prompt = "[INST] <image> \n What is shown in this image? Loading Models with CoreWeave’s Tensorizer; Frequently Asked Questions; Models. Here are two examples for using NVIDIA GPU and AMD GPU. OpenAI Compatible Server; Deploying with Docker; Distributed Inference and Serving; Production Metrics; Environment Variables; Usage Stats Collection; Examples# Scripts. Each vLLM instance only supports one task, even if the same model can be used for multiple tasks. 10 # You may lower either to run this example on lower-end GPUs. For example, if you have 4 GPUs in a single node, you can set the tensor parallel size to 4. 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 tensor parallel size is the number of GPUs you want to use. vLLM provides a robust framework for high-throughput serving, Multi-Modality#. MultiModalDataDict. You can pass a single image to the 'image' field Next to create the deployment file for vLLM to run the model server. PromptInputs. Name or path of the huggingface tokenizer to use. Llava Next Example# Source vllm-project/vllm. https://www. Debugging Tips. Ray serve's vLLM example does not work with multiple models and tensor parallelism. PP. ) During startup, dummy data is passed to the vLLM model to allocate memory. vLLM provides experimental support for multi-modal models through the vllm. Supported Models# vLLM supports generative and pooling models across various tasks. 4 5 Requires HuggingFace credentials for access to Llama2. 5. PromptType:. 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. The task to use the model for. 5-vision-instruct) 10 Multi-Modality#. Note that, as an inference engine, vLLM does not introduce new models. The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM. 3. 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. Right now vLLM is a serving engine for a single model. In such cases, you """ 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. Aquila, Aquila2. Testing Models. Quick Start. 3 4 Launch the vLLM server with the following command: 5 6 (single image inference with Llava) 7 vllm serve llava-hf/llava-1. Models. prompt: The prompt should follow the format that is documented on HuggingFace. 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 5. multimodal. To get started with LiteLLM and VLLM, you need to set up your environment and make a simple API call. . Currently, vLLM only has built-in support for image data. Supported Hardware for Quantization Kernels; AutoAWQ; BitsAndBytes; INT8 W8A8 Supported Models# vLLM supports generative and pooling models across various tasks. Image#. All models utilized by vLLM are sourced from third-party providers. See this RFC for upcoming changes, and open an vLLM seamlessly supports many Huggingface models, including the following architectures: Baichuan ( baichuan-inc/Baichuan-7B , baichuan-inc/Baichuan-13B-Chat , etc. , a new attention mechanism), the process can be a bit more complex. PromptStrictInputs accepts an additional attribute multi_modal_data which allows vLLM provides experimental support for Vision Language Models (VLMs), allowing users to deploy multiple models efficiently. """ The complexity of adding a new model depends heavily on the model’s architecture. If the service is correctly deployed, you should receive a . To do this, substitute your model’s linear and embedding layers with their tensor-parallel versions. When working with vLLM, there are several levels of testing available for models. Sometimes, there is a need to process inputs at the LLMEngine level before they are passed to the model executor. LoRA. AquilaForCausalLM. ) BLOOM ( bigscience/bloom , bigscience/bloomz , etc. You can register input Offline Inference#. It uses the OpenAI Chat Completions API, which easily integrates with other LLM tools. The process is considerably straightforward if the model shares a similar architecture with an existing model vLLM provides experimental support for multi-modal models through the vllm. next. To enable distributed inference the following additions need to made to the model-config. 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. Therefore, all models supported by vLLM are third-party models in this regard. 5 """ 6 from argparse import Namespace 7 from typing import List, NamedTuple, Optional 8 9 from PIL. vllm. 11 12 Multi-Modality#. 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. Loading Models with CoreWeave’s Tensorizer; Frequently Asked Questions; Models. yaml of the examples where 4 is the number of desired GPUs to use for the inference: # The complexity of adding a new model depends heavily on the model’s architecture. gdxntounmymvxnfuyjvwonfxmexwagwpgfxuesaptibvehhwqr