OSS Radar
SponsorYour product here, sponsor OSS Radar →
hiyouga avatar

hiyouga/LlamaFactory

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024). Why it matters: useful plumbing for shipping LLM-powered features.

GitHub description: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)

Stars

75k

Last 24h

+61est.

Last 7 days

+428est.

Forks

9.2k

Star history (last 1 snapshot)

Star history for hiyouga/LlamaFactory

Only one snapshot so far. The chart fills in as OSS Radar collects daily snapshots.

est. means the star gain is estimated, not measured: until OSS Radar has collected enough daily snapshots for a repo, we use total stars ÷ days since creation (or extrapolate from the snapshots we do have). Estimates are replaced by measured deltas automatically as history builds up.

Maintainer? Add the badge to your README

Shows your live OSS Radar rank and links back here. Updates hourly.

OSS Radar badge preview
[![Trending on OSS Radar](https://ossradar.net/badge/hiyouga/LlamaFactory)](https://ossradar.net/repo/hiyouga/LlamaFactory)
<a href="https://ossradar.net/repo/hiyouga/LlamaFactory"><img src="https://ossradar.net/badge/hiyouga/LlamaFactory" alt="Trending on OSS Radar"></a>

About LlamaFactory

hiyouga/LlamaFactory is a Python project in the LLM tooling category, created 3 years ago. It has 75k GitHub stars and 9.2k forks, and gained +428 stars over the last 7 days (early estimate), ranking #93 of 167 LLM tooling repos tracked by OSS Radar this week.

See more trending LLM tooling repos or the top AI repos this week.

Language
Python
Category
LLM tooling
Created
May 28, 2023
Last push
Oct 10, 2026
  • agent
  • ai
  • deepseek
  • fine-tuning
  • gemma
  • gpt
  • instruction-tuning
  • large-language-models
  • llama
  • llama3
  • llm
  • lora
  • moe
  • nlp
  • peft
  • qlora
  • quantization
  • qwen
  • rlhf
  • transformers

Similar rising repos

  • Local UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4, MiniMax-H3, Gemma 4, FLUX and more. Why it matters: useful plumbing for shipping LLM-powered features.

  • A high-throughput and memory-efficient inference and serving engine for LLMs. Why it matters: useful plumbing for shipping LLM-powered features.

  • Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU. Why it matters: useful plumbing for shipping LLM-powered features.

  • ollama/ollama+1.1kest.

    Get up and running with Kimi, GLM, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. Why it matters: useful plumbing for shipping LLM-powered features.

  • Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both… Why it matters: useful plumbing for shipping LLM-powered features.

  • SGLang is a high-performance serving framework for large language models and multimodal models. Why it matters: useful plumbing for shipping LLM-powered features.