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← Topicランキング · 2026-08
GitHub TOPIC

#fine-tuning

GitHub Topic「fine-tuning」がついているAI関連リポジトリの集計。Topicはリポジトリ作者が自己申告するメタタグで、AI関連の文脈で何が「fine-tuning」と分類されているかを可視化します。

11
タグ付Repo
5
TOP11合計★
1
AIツール痕跡あり
11
TOP表示数

REPOS #fine-tuning のRepo (TOP 11 / Stars降順)

markl-a/spectyn-training

Alpha · Tier 1 — Agentic post-training orchestrator on phantom-mesh. Self-hosted, cross-device, agentic fine-tuning framework (LoRA/Unsloth).

Python 1 AI 100
ngu-gif/genai-role-playbook

GenAI Career Roadmap 2026 🚀 | AI Job Paths & Skills Guide

HTML 1 AI 100
iiTzAK/accounting-copilot

Fine-tuning an open 7B LLM into a GST question-answering assistant, with a reproducible eval harness measuring the gain over the base model.

Python 1 AI 100
inecore/AMK

Agent Memory Kit System: Capture every correction and interaction to build your own SLM from day one

Python 1 AI 70
feRpicoral/anvil

Production-grade LLM fine-tuning with LoRA/QLoRA — dataset synthesis, rigorous eval, cost analysis

Python 1 AI 70
wane528/trace2train

Local CLI to turn failed AI agent traces (wrong tool, bad args, over-refusals) from LangSmith/Langfuse into clean SFT/DPO fine-tuning data

Python 0 AI 100 個人 公開済 ↗
MehmetnC1/llm-turkce-asistan

Kendi fine-tune ettiğim Türkçe LLM — Qwen2.5-3B + QLoRA (Unsloth), Kaggle T4'te eğitildi, Ollama ile yerelde çalışıyor

Jupyter Notebook 0 AI 100
chandlertee/noetica

Self-hosted local-AI stack over Ollama: private chat, a structured-output API, and a QLoRA fine-tune → serve → chat loop. No cloud, no secrets.

Python 0 AI 70 1 sig
Tabish5858/slm-vs-frontier-entity-res-

Fine-tuned 3B model + RAG beats Claude Opus 4.8 raw at entity resolution (99% vs 78.5%) on unseen companies — Covent LLM Challenge submission.

Python 0 AI 70
mkupermann/souprise

Your local and open source business AI toolkit for finetuning, analysis and...synthetic data, LoRA fine-tuning workflow, HDC retrieval, offline RAG

Python 0 AI 70
openatlaspro-AI/mlx-lora-finetune

LoRA fine-tuning of Qwen2.5 (4-bit) with Apple MLX for freight-text→JSON extraction — honest before/after eval (71%→100% on held-out synthetic set), every number a committed receipt.

Python 0 AI 35

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集計対象: 各Repoの最新contentスナップショットの topics_json に小文字一致でマッチしたもの。 算出方法