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