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lm-sys avatar

lm-sys/FastChat

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39,472 stars·4,789 forks·Python·Apache-2.0·73 views

FastChat

FastChat is a training and serving platform for large language models that provides an integrated toolkit for fine-tuning, hosting, and benchmarking chatbots. It functions as an inference server capable of hosting multiple models and exposing them via a standardized API for chat applications.

The platform distinguishes itself through a distributed model controller that manages worker nodes and routes requests across a hardware-agnostic inference layer supporting various accelerators. It includes a dedicated evaluation framework for assessing model quality using automated judges, multi-turn dialogue benchmarking, and side-by-side preference ranking for human-driven comparisons.

The system also covers model specialization through a fine-tuning toolkit that utilizes low-rank adaptation to reduce training memory requirements. For deployment and access, it provides an OpenAI-compatible REST API and a web interface for distributed user interactions, as well as a command line interface for local inference.

Features

  • Large Language Model Serving - Hosts multiple language models via web interfaces and network APIs to provide chat capabilities to distributed users.
  • OpenAI-Compatible APIs - Implements a standardized network interface that is compatible with the OpenAI API for diverse model backends.
  • Chatbot Hosting Services - Provides a web interface and network API for hosting multiple language models to facilitate distributed user interactions.
  • Distributed Model Orchestration - Manages a network of worker nodes hosting different language models and routes requests based on availability.
  • Fine-Tuning Toolkits - Ships a toolkit of scripts and tools for training specialized models using low-rank adaptation.
  • Inference Execution - Implements an interface for executing language model interactions across various hardware accelerators.
  • Language Model Fine-Tuning - Provides frameworks and utilities for specializing large language models using custom datasets and memory-efficient tuning.
  • Model Inference Servers - Functions as a dedicated server that hosts multiple LLMs and exposes them via a standardized API.
  • Language Model Fine-Tuning - Enables the creation of specialized chatbots by fine-tuning models with custom datasets and LoRA.
  • Parameter Efficient Fine-Tuning - Implements low-rank adaptation (LoRA) to reduce training memory requirements during model specialization.
  • Model Deployment and Platforms - Provides an integrated platform for fine-tuning, hosting, and benchmarking large language models.
  • LLM Evaluation - Implements a system for assessing LLM quality using automated judges and human-driven side-by-side comparisons.
  • Automated Model Judges - Uses automated judges and multi-turn question sets to assess the quality and accuracy of chatbot responses.
  • Command Line Inference Interfaces - Provides a command line interface for running large language models on local hardware for private interaction.
  • Model Performance Benchmarking - Implements a benchmarking system for assessing response accuracy using multi-turn question sets and automated judges.
  • Model Benchmarking - Provides tools for comparing different language model outputs side-by-side to determine performance preference.
  • Hardware-Agnostic Inference Layers - Provides an abstraction layer that decouples model execution logic from specific GPU, CPU, or NPU hardware backends.
  • Side-By-Side Preference Ranking - Captures human evaluation data by presenting two anonymous model outputs for blind comparison and voting.
  • Side-by-Side Diff Viewers - Provides a side-by-side interface for comparing model outputs through user voting to determine preference.
  • Multi-Turn Dialogue Benchmarking - Assesses model quality by running structured conversations through predefined questions and automated judging scripts.
  • AI & Machine Learning - Platform for training and serving large language model chatbots.
  • Distillation Algorithms - Distills high-quality conversational capabilities into open-source models.
  • Evaluation Frameworks - Platform for training, serving, and evaluating conversational chatbots.
  • Foundation Models - Open-source chat-optimized model based on Llama.
  • Generative Reward Models - Framework for evaluating LLMs as judges.
  • Inference and Serving - Distributed serving system with web UI and API support.
  • Language Model Development - Platform for training, serving, and evaluating LLM chatbots.
  • Language Models - An open platform for training, serving, and evaluating chat models.
  • Large Language Models - Platform for training and serving chat-based language models.
  • LLM Development and Research - Platform for training and serving open-source chatbots.
  • Model Training - Platform for training, serving, and evaluating language models.
  • Open Source Models - Provides a platform for training and serving chat-based models.
  • Web Applications - Platform for training, serving, and evaluating large language models.
  • AI Cloud Infrastructure - Open platform for training, serving, and evaluating language models.
  • Educational Resources - Platform for serving and evaluating large language model chatbots.
  • Large Language Models (LLMs) - Listed in the “Large Language Models (LLMs)” section of the The Incredible Pytorch awesome list.

Star history

Star history chart for lm-sys/fastchatStar history chart for lm-sys/fastchat

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with FastChat

These projects share indexed features with FastChat. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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    Oumi is a comprehensive large language model development platform designed for synthesizing data, fine-tuning models, and running performance evaluations. It serves as a unified environment for the entire model lifecycle, encompassing a training and fine-tuning suite, an evaluation framework, and tools for synthetic data generation and model distillation. The platform is distinguished by its iterative, failure-driven synthesis approach, which analyzes model weaknesses during evaluation to generate targeted training data. It utilizes an LLM-based judge framework to programmatically score respo

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    OpenLMLab/MOSS

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    MOSS is a conversational AI platform, fine-tuning toolkit, and quantized model runtime. It provides a framework for deploying large language models capable of multi-turn dialogue, general-purpose response generation, and following complex instructions. The system functions as a tool-augmented framework that extends model knowledge through external plugins and tool-call loops. This allows the model to execute tasks via search engines and calculators to augment responses with external data. The project covers model training through supervised conversational fine-tuning and optimizes deployment

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    hiyouga/LLaMA-Factory

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    LLaMA-Factory is a comprehensive suite for dataset preparation, model fine-tuning, memory optimization, and standardized API deployment. It provides a unified platform for the supervised and reward-based fine-tuning of large language models and vision-language models. The framework includes a specialized toolkit for training vision-language models and a model serving interface that deploys trained models through high-performance APIs. It utilizes precision tuning and quantization techniques to reduce the hardware requirements and memory footprint of large models. The system covers data pipel

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Frequently asked questions

What does lm-sys/fastchat do?

FastChat is a training and serving platform for large language models that provides an integrated toolkit for fine-tuning, hosting, and benchmarking chatbots. It functions as an inference server capable of hosting multiple models and exposing them via a standardized API for chat applications.

What are the main features of lm-sys/fastchat?

The main features of lm-sys/fastchat are: Large Language Model Serving, OpenAI-Compatible APIs, Chatbot Hosting Services, Distributed Model Orchestration, Fine-Tuning Toolkits, Inference Execution, Language Model Fine-Tuning, Model Inference Servers.

Which projects share features with lm-sys/fastchat?

Projects with overlapping indexed features include: paddlepaddle/paddlenlp — PaddleNLP is a development library and toolkit for training, fine-tuning, and deploying large and small language… oumi-ai/oumi — Oumi is a comprehensive large language model development platform designed for synthesizing data, fine-tuning models,… openlmlab/moss — MOSS is a conversational AI platform, fine-tuning toolkit, and quantized model runtime. It provides a framework for… hiyouga/llama-factory — LLaMA-Factory is a comprehensive suite for dataset preparation, model fine-tuning, memory optimization, and… databrickslabs/dolly — Dolly is an instruction-tuned large language model designed to follow complex natural language directions. It operates… unslothai/unsloth — Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and…