The following is the list of model architectures that are currently supported by vLLM. vLLM is a library that speeds up the inference of large language models (LLMs) on GPUs. Deploying Llama2 using vLLM vLLM is an open-source LLM inference and serving library. 可以使用vLLM作为LLM后端进行推理，需要额外安装vLLM库（安装过程8-10分钟）。 But vLLM is not without its limitations. It outperforms vLLM-packed by up to 4 times for a few adapters and up to 30 times over PEFT while accommodating a significantly larger adapter count. In this article, we will delve into the context in which LoRA has arisen, its. PathLike) — The name of the PEFT configuration to use. org. json. schema. Tutorials on finetuning, including full-parameter tuning, LoRA, and Q-LoRA. pt") ['module']. As an important component in the BentoML ecosystem, OpenLLM follows a similar workflow as BentoML for users to ship models to production. LongLLaMA is built upon the foundation of OpenLLaMA and fine-tuned using the Focused Transformer (FoT) method. . from langchain. Reload to refresh your session. How to merge LoRa weights with base model? 🤗Transformers. At its core, vLLM is built to provide a solution for efficient LLM inference and serving. cpp. 0 →. Finetuning LLMs with LoRA and QLoRA: Insights from Hundreds of Experiments - Lightning AI. 7 and Baichuan2 has default configuration of repetition penalty 1. You will need 24 GB of CPU RAM. Just pick the lora in the list and it will add it to the text box with a weight of 1. Continuous batching builds on the idea of using a bigger batch size and goes a step further by immediately tackling new tasks as they come in. Reinforcement learning from Human Feedback (also referenced as RL from human preferences) is a challenging concept because it involves a multiple-model training process and different. For Q-LoRA, we advise you to load our provided quantized model, e. When the app is running, all models are automatically served on localhost:11434. 24xlarge node. to("cuda"). Dec 3, 1969 - Jul 4, 2018. How continuous batching enables 23x throughput in LLM inference while reducing p50 latency. Large language models such as GPT-3, which have billions of parameters, are often run on specialized hardware such as GPUs or. Now you can use FastChat as a local drop-in replacement for OpenAI APIs. Activity is a relative number indicating how actively a project is being developed. chat import (. It is licensed under Apache 2. I believe this PR does what you want: #289. Here are some key points to consider: Use vLLM when maximum speed is required for batched prompt delivery. 0 and can be accessed from GitHub and ReadTheDocs. No branches or pull requests. engine. ;. I have trained falcon 7b model with qlora but the inference time for outputs is too high. 闻达：一个LLM调用平台。目标为针对特定环境的高效内容生成，同时考虑个人和中小企业的计算资源局限性，以及知识安全和私密性问题 - GitHub - wenda-LLM/wenda: 闻达：一个LLM调用平台。目标为针对特定环境的高效内容生成，同时考虑个人和中小企业的计算资源局限性，以及知识安全和私密性问题Tuning LLMs with no tears 💦. The container comes equipped with multiple backend inferencing engines, including vLLM, DeepSpeed-FastGen and Hugging Face, to cover a wide variety of. prompts. Covers AITemplate, nvFuser, TensorRT, FlashAttention. , Prefix Tuning, P-Tuning, Prompt Tuning). All reactions. Just for reference this was the solution I used. Check mark the one you want to merge to Checkpoint A. py","contentType":"file"},{"name. We manage the distributed runtime with Ray. 0 is not compatiable with currrent vllm Baichuan model. vLLM is a high-throughput and memory-efficient LLM serving engine. features enable S-LoRA to serve thousands of LoRA adapters on a single GPU or across multiple GPUs with a small overhead. Kubeflow is an end-to-end ML platform for Kubernetes; it provides components for each stage in the ML lifecycle, from exploration to training and deployment. All you need to do is provide the token <lora:xyz> in the prompt to load and activate the LoRA. In the end with quantization and parameter efficient fine-tuning it only took up 13gb on a single GPU. 5 bytes). However, an alternative practice involves. Finally, see how fine-tuning through Reinforcement Learning from Human Feedback (RLHF). from_pretrained ('. It’s likely that you can fine-tune the Llama 2-13B model using LoRA or QLoRA fine-tuning with a single consumer GPU with 24GB of memory, and using QLoRA. g. See full list on github. No one assigned. 1. I also used --gpus all flag when running. Saved searches Use saved searches to filter your results more quicklyLoRA implemented via SGMV is efficient and preserves the strong batching effect. 问题11：模型合并之后SHA256不一致. In other words, you would need cloud computing to. entrypoints. 模型量化：参考ChatGLM的量化代码，对Chinese-llama2模型进行量化。详见量化部署代码; gradio demo代码：见gradio demo code vLLM is an open-source library that allows you to use HuggingFace models for fast and easy LLM inference and serving. Compared to state-of-the-art libraries such as HuggingFace PEFT and vLLM (with naive support of LoRA serving), S-LoRA can improve the throughput by up to 4 times and increase the number of servedlmdeploy and vllm have custom backends for Nvidia Triton Inference Server, which then actually serves up models. I plan to use a finetuned FLAN-T5 model. The “missing” graph for the full. lmdeploy and vllm have custom backends for Nvidia Triton Inference Server, which then actually serves up models. Q&A for work. The fine-tuning repository mentioned below provided a way to load the trained model by combining the original model and the learned parameters. vllm 部署：模型部署采用huggingface原生代码效率比较慢，为了获得2. Following is the deployment workflow from a high-level. 2 participants. 有人使用vLLM加速过自己的大语言模型吗？. • 20 hr. Is this perhaps a lora/peft model issue? I was looking at #182 and #289 to see if it works with the finetuned model. Deploying Llama2 using vLLM vLLM is an open-source LLM inference and serving library. Melanie Kambadur. Instructions on deployment, with the example of vLLM and FastChat. Saved searches Use saved searches to filter your results more quicklyvLLM supports a variety of generative Transformer models in HuggingFace Transformers. 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data. Introducing Lit-GPT: Hackable implementation of open-source large language models released under Apache 2. Here, we also apply LoRA (Low Rank Adaptation) to reduce model size. I'll. py","path":"examples/api_client. A particularly intriguing feature of LLaMA 2 is its employment of Ghost Attention (GAtt). vllm-project. There is no problem with using huggingface repo model with vLLM, but when I changed huggingface model_id to local model path, vLLM checked the model at huggingface repo, "does not appear to have a file named config. bin文件）。执行以下命令：Let's say you have base model B, finetuned Dreambooth model A and much better mixed model C that you want to improve by adding the training from that finetuned model into it. vllm. 效果怎么样？. 88s latency. From a computational standpoint, given the marginal increase in parameters compared to the pre-trained model, the additional computational load is relatively small. How continuous batching enables 23x throughput in LLM inference while reducing p50 latency. You switched accounts on another tab or window. Save Page Now. Then pick your checkpoint and click merge to checkpoint. Imagine thousands of tailored LLMs can come to life on a single GPU, each. 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC. At a high level, LoRA accomplishes two things. When fine-tuning with LoRA, it is possible to target specific modules in the model architecture. , ollama pull llama2. Visual Instruction Tuning. ⬆ back to ToC. The model could fit into 2 consumer GPUs. Follow Veteran. , Prefix Tuning, P-Tuning, Prompt Tuning). lmsys. RWKV is a RNN with transformer-level LLM performance. They should help users who want to run inference in projects like. Activity is a relative number indicating how actively a project is being developed. engine. There are many ways to serve LLMs, but combining vLLM and AutoAWQ sets a new benchmark in serving LLMs, according to. Deploy and Fine Tune Llama 2 on your cloud. 合并lora模型出现这个问题. vLLM demo frontends: List of inputs as OpenAI input Langchain passes prompt as a list instead of str #186 Possibility of Passing Prompts as List[str] to AsyncEngine. The adaptation process will target these modules and apply the update matrices to them. RLHF with LoRA is a unique application for ZeRO++ since most model weights are frozen. Assuming T is the total time, B is the batch size, L is the decoded sequence length. To use the vLLM backend, you need a GPU with at least the Ampere architecture or newer and CUDA version 11. More instructions to train other models (e. We are actively working on system. pth文件）或者输出HuggingFace版本权重（. github. Lack of support for adapters (LoRA, QLoRA, etc. Aurelien Rodriguez. 1. , `cp38`, `cp39`, `cp311`). inspiration arises from the comparison between p-tuning (Liu et al. This achieves 82. Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. md. For the 7B and 13B models, LoRA consumes much less memory and can, therefore, be run on fewer or cheaper instances. In contrast, LLaMA 2, though proficient, offers outputs reminiscent of a more basic, school-level assessment. In this paper, we present the first attempt to use language-only GPT-4 to generate multimodal language-image. llms. For FastApi, we used two workers to serve the model. On our preliminary evaluation of single-turn instruction following, Alpaca behaves qualitatively similarly to OpenAI’s text-davinci-003, while being surprisingly small and easy/cheap to reproduce (<600$). Ollama. LLaMA2-Accessory: An Open-source Toolkit for LLM Development 🚀. There is a bit of confusion of whether or not to use quantization when loading the model for fine tuning, apparently vLLM does not work with quantized models. Below is an example comparison. Closed. {"payload":{"allShortcutsEnabled":false,"fileTree":{"scripts/openai_server_demo":{"items":[{"name":"README. base import BaseLLM from langchain. Which means an additional 16GB memory goes into quant overheads, activations & grad. Latency represents how long the user should wait to get the response from. It is designed to give developers a space to experiment with building new large language models, the bedrock of. vLLM [1] ：适用于大批量Prompt输入，并对推理速度要求高的场景；. llms. So I want to use vllm for increasing the inference time for that I have used a. - GitHub - Blealtan/RWKV-LM-LoRA: RWKV is a RNN with transformer-level LLM. Illustration of differences in total required memory when fine-tuning the Llama 2 model series with a context length of 512 tokens and a batch size of 8 on a single p4de. ray_utils import DeviceID, initialize_cluster, ray ImportError: cannot import name 'DeviceID' from 'vllm. To review, open the file in an editor that reveals hidden. For a complete list of supported models and model variants, see the Ollama model. It utilizes PagedAttention , a new attention algorithm that effectively manages attention keys and values, making it achieve exceptionally high throughput without requiring any model architecture changes. You either need a backend with good batching support (vLLM), or if you don't need much throughput, an extremely low end GPU or no GPU at all for exLlama/llama. from langchain. A high-throughput and memory-efficient inference and serving engine for LLMs. You switched accounts on another tab or window. Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. com vLLM-packed: Because vLLM does not support LoRA, we merge the LoRA weights into the base model and serve the multiple versions of the merged weights separately. , FastChat-T5) and use LoRA are in docs/training. For the 7B and 13B models, LoRA consumes much less memory and can, therefore, be run on fewer or cheaper instances. WEB DEMO。 本实现基于vLLM部署LLM后端服务，暂不支持加载LoRA模型、仅CPU部署和使用8bit、4bit. vllm 部署：模型部署采用huggingface原生代码效率比较慢，为了获得2. It does not support LoRA and QLoRA adapters, which are popular techniques for fine-tuning open-source LLMs without modifying the original model weights. And , the lora model has been merged into the Qwen-7B-Chat model and exported as a new model, which is saved. Talk to the Veterans Crisis Line now. Collectively, these features enable S-LoRA to serve thousands of LoRA adapters on a single GPU or across multiple GPUs with a small overhead. . . Stars - the number of stars that a project has on GitHub. Code Llama is built on top of Llama 2 and is available in three models: Code Llama, the foundational code model; Codel Llama - Python. LoRA augments a linear projection through an additional factorized projection. However, I've run into a snag with my LoRA fine-tuned model. fastllm 介绍 功能概述 两行代码加速 （测试中，暂时只支持ubuntu） PEFT支持(测试中，目前仅支持ChatGLM + LoRA) 推理速度 CMMLU精度测试 快速开始 编译 运行demo程序 简易python调用 Python绑定API 多卡部署 fastllm_pytools中使用多卡部署 Python绑定API中使用多卡部署 c++. llms. From saving memory with QLoRA to selecting the. You signed out in another tab or window. 24xlarge is equipped with 4 NICs, and each has 100 Gbps throughput. LoRa) supported in HuggingFace's PEFT library. vllm Public. For example, llama-7b with bnb int8 quant is of size ~7. However, LoRA presents some tangible advantages relative to the original Dreambooth: · Faster training. schema. Or even for one user, they can hold many. 8. Fit Your LLM on a single GPU with Gradient Checkpointing, LoRA, and Quantization: a deep dive. Compared to state-of-the-art libraries such as HuggingFace PEFT and vLLM (with naive support of LoRA serving), S-LoRA can improve the throughput by up to 4 times and increase the number of served. from typing import Any, Dict, List, Optional from langchain. Discover and share open-source machine learning models from the community that. Workflow. bin as the Hugging Face format and modified the code to ignore the LoRA, but I couldn't achieve the desired result. LLaVA: Large Language-and-Vision Assistant. A fully revamped checkpoint based on the 512dim lora and chilloutmix_NiPrunedFp32Fix + deliberate_v2. 最新版本的 TGI 在加入了 PagedAttention 之后，吞. ; Suggest or implement. This notebook covers how to get started with vLLM chat models using langchain's ChatOpenAI as it is. Capture a web page as it appears now for use as a trusted citation in the future. We will not only reduce computational and storage overheads but also mitigate the phenomenon of catastrophic forgetting observed during extensive fine-tuning. 具体的には. Introduction to DashScope API service, as well as the instructions on building an OpenAI-style API for your model. You signed out in another tab or window. cpp工具为例，介绍模型量化并在本地CPU上部署的详细步骤。 Windows则可能需要cmake等编译工具的安装（Windows用户出现模型无法理解中文或生成速度特别慢时请参考FAQ#6）。 本地快速部署体验推荐使用经过指令精调的Alpaca模型，有条件的推荐使用8-bit模型，效果更佳。本文对 vllm 和 TGI 两个开源方案进行了实践测试，并整理了一些部署的坑。. Follow their code on GitHub. In order to leverage LoRA, we will use Hugging Face’s PEFT library that also supports other methods similar to LoRA for fine-tuning (e. There are lots of LLM providers (OpenAI, Cohere, Hugging Face, etc) - the LLM class is designed to provide a standard interface for all of them. path. HTML 3 MIT 3 0 0 Updated 2 days ago. Xiaoqing Ellen Tan. py:593 -- Detecting docker specified CPUs. 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data. Despite these limitations, vLLM. /README_EN. Until recently, this work has been executed on Nvidia* GPUs with CUDA. And needless to say there has been a lot of request for lora deployments You can build and run vLLM from source via the provided dockerfile. vLLM supports distributed tensor-parallel inference and serving. To build vLLM: $ DOCKER_BUILDKIT = 1 docker build. Hugging Face command-line interface: FastChat provides a simple command-line. Excellent job, it made my LLM blazing fast. In the ever-evolving realm of large language models (LLMs), a concept known as Low-Rank Adaptation (LoRA) has emerged as a groundbreaking technique that empowers LLMs and other generative-AI models to adapt and fine-tune their behavior with precision. vLLM supports a variety of generative Transformer models in HuggingFace Transformers. In previous versions of Ray, CPU detection in containers was incorrect. 特点： ; 快速的推理速度 ; 高效的kv cache ; 连续的batch请求推理 ; 优化cuda算子 ; 支持分布式推理 第一步： . Asking for help, clarification, or responding to other answers. py --sd_model . py脚本将LoRA权重合并，那么无需再指定--lora_model： 使用vLLM加速推理. md","path":"scripts/openai_server_demo/README. I have so far used Langchain with the OpenAI (with 'text-davinci-003') apis and Chromadb and got it to work. The model should be initialized with the from_pretrained method from the 珞 Transformers library. This guide will run the chat version on the models, and. vLLM is fast with: State-of-the-art serving throughput; Efficient management of attention key and value memory with PagedAttention;Qwen-VL (Qwen Large Vision Language Model) is the multimodal version of the large model series, Qwen (abbr. Other models, platforms and LoRA support. The difference between TGI and vLLM increases with bigger models. 显示全部 . The success of Transformer models has pushed the deep learning model scale to billions of parameters. To load unmerged LoRA weights, you simply need to pass an additional argument --model-base , which is the base LLM that is used to train the LoRA weights. May 30, 2023. It enables you to fine-tune even a 65-billion-parameter model on a single 48GB GPU. The Colossal. lora_train1model. Development. It queries LLMs using pre-defined prompts and asks GPT-4 to judge which LLM's response is. Evaluating with publicly available prompts ensures reproducibility and comparability between papers. Within this toolkit, we present SPHINX, a. get_base_model () Load original llama to vllm with llm = LLM ("llama-7b"). LORA are separate adapter weights - assuming enough memory, you should be able to handle multiple of them and the base weights then just use separate LORA memory spaces for different inferences. 05, text generation outputs cannot be replicated 1 to 1. vLLM-packed: Because vLLM does not support LoRA, we merge the LoRA weights into the base model and serve the multiple versions of the merged weights. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vllm/entrypoints":{"items":[{"name":"openai","path":"vllm/entrypoints/openai","contentType":"directory"},{"name. Simple enough. There are two important PEFT methods: LoRA (Low Rank Adaptation) and QLoRA (Quantized LoRA), where pre-trained models are loaded to GPU as quantized 8-bit and 4-bit weights, respectively. · It only requires 5–10 images to infer the style. py的63行处SamplingParams的默认值. I then tried to use it along with LangChain and LlamaIndex. To run distributed inference, install Ray with: $ pip install ray. Hi, I’m Virginia. g. 提示语替换为 You are a helpful assistant. vllm同样是GPU推理的方案。相比较与FasterTrainsformer，vllm更加的简单易用。不需要额外进行模型的转换。支持fp16推理。 . 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data. For example, let’s say that your GPU has a batch size of 4 meaning it. There are 30+ professionals named "Victoria Lora", who use LinkedIn to exchange information, ideas,. cpp, text-generation-webui, LangChain, privateGPT, vLLM等LLaMA. In other words, you would need cloud computing to fine-tune your models. Supported Bard and Claude. Let's look at the usage and the common culprit you may encounter while trying to set things up. TensorRT-LLM is an open-source library that runs on NVIDIA Tensor Core GPUs. --target vllm-openai--tag vllm/vllm-openai--build-arg max_jobs = 8 Checkpoint export (merge_lora_checkpoint. Qwen-VL accepts image, text, and bounding box as inputs, outputs text and bounding box. Below there should be a list of Lora's you have in your lora directory. vLLM, and Faster Transformers achieve 3x. (Optional): Advanced Features, Third Party UI. If possible, use libraries for LLM inference and serving, such as Text Generation Inference, DeepSpeed, or vLLM. Develop. 21 per hour, or about $900 per month to serve 24x7. Integrations# Dify: an LLMOps platform that enables developers (and even non-developers) to quickly build useful applications based on large language models, ensuring they are visual, operable, and improvable. Learn more about Teams{"payload":{"allShortcutsEnabled":false,"fileTree":{"examples":{"items":[{"name":"api_client. It offers OpenAI compatible API, which makes it easy to be integrated with the existing LLM applications. It is licensed under Apache 2. Viola Mallory Lawrence (December 2, 1894, New York City – November 20, 1973) is considered by many to be the first female film editor in Hollywood. 2. As stated in the introduction, PEFT methods have made it possible to fine-tune LLMs at a low cost. The vigogne (French name for vicuña) is a South American camelid native to the Andes Mountains. This server can be queried in the same format as OpenAI API. Below are useful metrics to measure inference speed. HuggingFace PEFTやvLLM（LoRAサービングを素朴にサポート）のような最先端のライブラリと比較して、S-LoRAはスループットを最大4倍向上さ. The adapter weights are uploaded to HF, and the base model I'm using is h2oai/h2ogpt-4096-llama2-13b-chat. Page 1. Deploying Llama2 using vLLM vLLM is an open-source LLM inference and serving library. Save Page Now. Civitai had like an application form for llm, trying to get people on board who make their own fine-tunes, Loras, etc. I’m running Ubuntu with WSL 2. LangChain does not serve its own LLMs, but rather provides a standard interface for interacting with many different LLMs. python server. Llama 2 is an open source LLM family from Meta. Imagine thousands of tailored LLMs can come to life on a single GPU, each. features enable S-LoRA to serve thousands of LoRA adapters on a single GPU or across multiple GPUs with a small overhead. In this paper, I. from langchain. Fine-tuning on Any Cloud with SkyPilot. {"payload":{"allShortcutsEnabled":false,"fileTree":{"examples":{"items":[{"name":"api_client. In order to share data between the different devices of a NCCL group, NCCL might fall back to. py --model_name_or_path baichuan-7B模型文件夹路径或huggingface地址 --do_train --dataset alpaca_gpt4_zh. You can use the following command to train Vicuna-7B using QLoRA using ZeRO2. It works by inserting a smaller number of new weights into the model and only these are trained. We’ve started using LoRA in all of our open source LLM training. 9. Large Language Models (LLMs) are a core component of LangChain. Tested throughput of llama-7b with single A100 40G, the result is 1. Excellent job, it made my LLM blazing fast. Currently vLLM does not support merging LoRA weights. Currently, we do not natively support. As the time of writing, Llama. It optimizes setup and configuration details, including GPU usage. There are several ways you can contribute to the project: ; Identify and report any issues or bugs. FastChat-T5) and use LoRA are in docs/training. 2 participants. LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance. Source code for langchain. {"payload":{"allShortcutsEnabled":false,"fileTree":{"docs/source/GetStarted":{"items":[{"name":"resources","path":"docs/source/GetStarted/resources","contentType. Saved searches Use saved searches to filter your results more quickly{"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". Chinese-LLaMA-2是在Llama-2的基础上，利用大规模中文数据进行增量预训练。. I got this message when trying out vllm with windows; No CUDA runtime is found, using CUDA_HOME='C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11. This repo is mainly inherited from LLaMA-Adapter with more advanced features. Check out our blog post. Reload to refresh your session. base import BaseLLM from langchain. 2xlarge to serve a custom llama-2-7b model will cost you $1. The value of “r” varies depending on the task, but in experiments, a value of 2~4 has been shown to yield good results. TLDR. 仿OpenAI API调用 (vLLM部署后端) 更加详细的OPENAI API. Connect and share knowledge within a single location that is structured and easy to search. 5. Different from full-parameter finetuning and LoRA, only fp16 is. (like PEFT). py","path":"vllm/model_executor/adapters/__init__. vLLM is a fast and easy-to-use library for LLM inference and serving. {"payload":{"allShortcutsEnabled":false,"fileTree":{"vllm/model_executor/adapters":{"items":[{"name":"__init__. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. , MPT-Chat-7B, Phoenix-inst-chat-7b) Other bug fixes. It streamlines fine-tuning by using low-rank decomposition to represent weight updates, thereby drastically reducing the number of trainable parameters. You can create LoRa from that finetuned Dreambooth model and base model to apply to C, but that loses a portion of training. The instructions here provide details, which we summarize: Download and run the app. Our default choice for serving models is vLLM, which provides high throughput and efficient memory management with continuous batching and Paged. Tongyi Qianwen), proposed by Alibaba Cloud. vLLM is a fast and easy-to-use library for LLM inference and serving. ago. Where do the models go when I download them?Loading and Usage. py. 3) 优于 vllm (v0. Fine-tuning on Any Cloud with SkyPilot. 请问在next_token = torch. The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives. I am struggling to do so. LoRA is a similar strategy to Adapter layers but it aims to further reduce the number of trainable parameters. Note: Actually, I’m also impressed by the improvement from HF to TGI. [2023/09] We released our PagedAttention paper on arXiv! [2023/08] We would like to express our sincere gratitude to Andreessen Horowitz (a16z) for providing a generous grant to support the. txt for the response from the server. Then enter the name you want the new checkpoint to have under "filename (option)". Adina Williams. From the benchmark, for LLama 2 70b, vLLM's downloading speed is 127s, which is far better than transformer's speed 600s when tested with p4de. FastChat-vLLM integration has powered LMSYS Vicuna and Chatbot Arena since mid-April. Learn more about releases in our docs. I guess both the approach that you have mentioned, works. 比HuggingFace快24倍！. so maybe something like that is coming at some point.