ローカルLLM
Unsloth
Run and fine-tune LLMs and diffusion models locally — faster training, smaller memory footprint.
★ 77.2k スターApache-2.0
ガイドを見る →can-i-finetune-thisは、DaoyuanLi2816が公開しているオープンソースのローカルLLMプロジェクトです（GitHub: DaoyuanLi2816/can-i-finetune-this）。GitHubスターは792、フォークは107で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年10月4日です。
Check if your GPU can fine-tune an LLM — memory estimates and LoRA recipes.
Set it up from the repo.
Tell it what hardware you have.
See memory needs for your target model.
Use the LoRA or QLoRA recipe it provides.
Know before you attempt the run.
Runnable recipes for your GPU.
Quantized fine-tuning guidance.
Built for consumer hardware.
Real numbers from your machine.
Avoid doomed training runs.
Download the source and move into the project folder.
git clone https://github.com/DaoyuanLi2816/can-i-finetune-this.git
cd can-i-finetune-thisEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project's launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandThat is exactly what the tool answers; run the estimator with your specs.
MIT licensed — commercial use allowed.
Quantized low-rank adaptation: fine-tuning that fits in much less VRAM.
They are designed to be; check the docs for your specific setup.
It focuses on single-GPU setups; check the docs for details.
Run and fine-tune LLMs and diffusion models locally — faster training, smaller memory footprint.
LLM inference in C/C++
Run AI models locally on your machine with node.js bindings for llama.cpp. Enforce a JSON schema on the model output on the generation level
Run a 70B LLM on a single 4GB GPU — layer-by-layer inference with almost no memory tricks skipped
Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.
Open bilingual (Chinese-English) 6B chat LLM with 32K-context variant and low-memory INT4 inference.
LibreAIカタログに掲載されたローカルLLMプロジェクトの中で最もスターの多いもの — can-i-finetune-thisが合わなかった場合に検討したい、現実的な候補リストです。
The simplest way to run open language models on your own machine.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
LLM inference in C/C++
A high-throughput and memory-efficient inference and serving engine for LLMs
🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.
カタログの中でcan-i-finetune-thisとスター数が近いローカルLLMプロジェクト — 前後の棚もそのまま見て回れます。
Local-first dashboard of tokens, models, and cost for coding agents.
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Local-first desktop workspace designed for running.
AI gets the context.
Desktop AI agent platform with one-click models and offline-first RAG.
Local code-intelligence MCP server and CLI for coding agents.