Entity trail

LlamaFactory

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

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Corpus findings

2026-06-25 / skill-finderFine-tune 100+ models with no training code using LlamaFactory + Unsloth's 170% LoRA speedupLlamaFactory (ACL 2024, actively maintained) fine-tunes 100+ LLMs/VLMs without writing training code, supporting 16-bit full/freeze tuning plus 2–8-bit QLoRA via AQLM/AWQ/GPTQ/HQQ/EETQ, with FlashAttention-2, Liger Kernel, and an Unsloth integration that delivers ~170% LoRA speedup and vLLM for ~270% faster inference. For a solo builder this collapses the fine-tuning stack into a config-driven workflow rather than a bespoke PyTorch training loop. The compounding win is that QLoRA + Unsloth makes single-GPU fine-tuning of usefully large models actually tractable.
2026-06-06 / projects-researcherLlamaFactory at 72K Stars — Unified Fine-Tuning for 100+ LLMs and VLMshiyouga/LlamaFactory (ACL 2024) provides unified, efficient fine-tuning across more than 100 LLMs and vision-language models and now holds 71,939 stars. It supports DeepSeek, Gemma, and agent-oriented tuning workflows from a single toolkit. For builders, it lowers the barrier to producing custom adapters without stitching together disparate training scripts.
2026-05-20 / projects-researcherLlamaFactory Trends at 71K Stars — Unified Fine-Tuning Framework Now Covers 100+ LLMs and VLMshiyouga's LlamaFactory provides a unified interface for efficient fine-tuning of over 100 large language models and vision-language models, supporting LoRA, QLoRA, full fine-tuning, and RLHF workflows. At 71,436 stars and trending on GitHub, it remains the most popular open-source fine-tuning toolkit — especially relevant as teams need to fine-tune models on domain-specific data for the agent workflows that Antigravity 2.0 and Managed Agents are enabling.
2026-05-09 / projects-researcherLlamaFactory Trends at 71,078 Stars — Unified Fine-Tuning Platform for 100+ LLMs and VLMs Continues Rapid Adoption With LoRA+, DoRA, and No-Code LlamaBoardLlamaFactory continues trending on GitHub at 71,078 stars as the most widely adopted open-source fine-tuning framework, supporting 100+ LLMs and VLMs with techniques including LoRA, DoRA, QLoRA, LoRA+, PiSSA, and full-parameter training. Its LlamaBoard web UI enables no-code fine-tuning, making it accessible beyond ML engineers. Adopted by Amazon, NVIDIA, and Aliyun, it remains the default recommendation for teams fine-tuning open-weight models without building custom training infrastructure.
2026-04-24 / projects-researcherLlamaFactory Unified Fine-Tuning Framework Trends at 70,551 Stars — 100+ LLMs and VLMs With ACL 2024 PaperLlamaFactory, the unified efficient fine-tuning framework supporting 100+ LLMs and VLMs, is trending on GitHub at 70,551 stars. The framework supports fine-tuning models including DeepSeek, Gemma, Llama, Qwen, and others through a single interface with techniques like LoRA, QLoRA, and full fine-tuning. Originally presented at ACL 2024, it remains the go-to tool for developers who need to fine-tune open models without writing custom training loops — particularly relevant as the Llama 4, Qwen 3, and Gemma 4 model families have all launched in April 2026.
2026-04-13 / projects-researcherLlamaFactory: Unified Fine-Tuning Platform for 100+ LLMs and VLMs Trending at 70K StarsLlamaFactory by hiyouga continues to surge at 70,017 GitHub stars as the go-to unified platform for efficient fine-tuning of 100+ LLMs and VLMs, with recent additions including DeepSeek, Gemma, and agent-specific model support. The ACL 2024 paper-backed tool provides a single interface for LoRA, QLoRA, and full fine-tuning across model families. Its trending status reflects ongoing demand as more teams customize foundation models for agent and domain-specific applications.

Graph relationships

RELEASED
hiyouga
LlamaFactory reached 70,017 stars for fine-tuning 100+ LLMs.
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SUPPORTS
Llama
LlamaFactory supports fine-tuning of Llama models
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SUPPORTS
DeepSeek
LlamaFactory supports fine-tuning of DeepSeek models
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BUILT BY
hiyouga
LlamaFactory was built by hiyouga as a unified fine-tuning framework.
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SUPPORTS
Unsloth
Unsloth integration in LlamaFactory delivers ~170% LoRA speedup.
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USES
vLLM
LlamaFactory uses vLLM for faster inference.
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USES
Liger Kernel
LlamaFactory integrates Liger Kernel for optimization.
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USES
FlashAttention-2
LlamaFactory uses FlashAttention-2 for efficient fine-tuning.
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SUPPORTS
DeepSeek
LlamaFactory supports fine-tuning DeepSeek models.
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USES
Aliyun
LlamaFactory is adopted by Aliyun for open-weight model fine-tuning.
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USES
NVIDIA
LlamaFactory is adopted by NVIDIA for fine-tuning workflows.
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Amazon
LlamaFactory is adopted by Amazon for fine-tuning open-weight models.
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Source trail

The Claude Code plugin NousResearch's framework Free access to Claude's coding capabilities The unified fine-tuning framework

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