An educational, end-to-end open-source knowledge base for LLM fine-tuning, dataset distillation, reinforcement learning, and local deployment.
🌐 Languages: English | 中文 | 한국어 | 日本語
🤗 Hugging Face: Jackrong
This repository is a growing educational resource portal for beginners and developers who want reproducible training pipelines, SFT and RL workflows including GRPO and GSPO, data preparation and distillation recipes, 16-bit export and GGUF deployment workflows, and agent-ready Qwen MTP GGUF conversion tools.
🚀 Start Here🗺️ Repository Map🏋️ Training Recipes✅ Supported Workflows🛣️ Model Support Roadmap⚙️ Qwen MTP GGUF Conversion Skill📘 Guides and Reports🧠 High-Fidelity Dataset Catalog🤝 Open-Source Commitment📚 Citation
| I want to... | Recommended entry |
|---|---|
| Fine-tune my first model in a browser | |
Open the GSPO Python tutorialBrowse data-processing recipesOpen the dataset catalogOpen the Qwen MTP GGUF SkillOpen the PDF guide libraryOpen the Codex Goal templates| Resource | What you will find | Entry | |---|---|---| | 🏋️ Training Recipes | SFT, GRPO, and GSPO notebooks and Python tutorials | |
OpenOpenOpenOpenOpenOpen| Model | Method | Environment | Quick setup | |---|---|---|---| | Qwopus3.5 27B | SFT | Google Colab | | | Qwopus3.6 27B | GSPO | Python script | | | Qwen3.5 9B | SFT | Kaggle | | | Qwopus3.5 35B | SFT | Kaggle | | | Llama3.2-R1 3B | GRPO | Kaggle |
Browse the full catalog in train_code/README.md.
| Workflow | Status | Documentation |
|---|---|---|
| SFT with LoRA / QLoRA | ✅ Released | |
Training recipesQwopus3.6 27B GSPO tutorialData-processing recipesTraining recipesTraining recipesMTP conversion skillReleased RL recipes may use GRPO or GSPO depending on the model and training objective.
| Model Family | SFT Support | RL Support |
|---|---|---|
| Qwen 3.5 | ✅ Released | Scheduled |
| Qwen 3.6 | ✅ Released | ✅ Released |
| Qwen 3 | Scheduled | Scheduled |
| Llama3.2-R1 3B | ✅ Included | ✅ Released |
| Llama 3.1 / 3.3 | Scheduled | Scheduled |
The qwen-mtp-gguf subproject supports Qwen-family MTP / nextn GGUF release workflows. It performs disk, RAM, tooling, token-access, and compatibility preflight checks, extracts compatible MTP tensors, injects them into the target model, converts with llama.cpp, smoke-tests outputs, quantizes releases, and supports safer upload/resume workflows.
🚀 Open the MTP Skill · 📖 Read the Pipeline Guide · 🤖 Read the Agent Usage Guide
Long-form PDFs live in the guide and technical report library.
| Guide | Topic | File |
|---|---|---|
| Qwopus3.5 27B Colab complete guide | Beginner-friendly end-to-end fine-tuning walkthrough | |
| Qwopus GLM 18B technical report | Model design and training notes |
The repository includes 24 curated high-fidelity datasets for reasoning, mathematics, coding, instruction following, conversation, and domain-specific distillation. Browse the full dataset catalog, or use download_datasets.py to batch download the suite for local training.
This project keeps the training source code and documentation for released fine-tuned models available so learners can reproduce, inspect, and adapt the workflows. The longer project philosophy and original message to builders are preserved in docs/PROJECT_PHILOSOPHY.md.
If you find this repository helpful in your learning or research, please consider citing it:
@misc{jackrong-llm-finetuning,
author = {Jackrong},
title = {Jackrong LLM Fine-Tuning Guide: An Educational LLM Fine-Tuning Knowledge Base},
year = {2026},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/R6410418/Jackrong-llm-finetuning-guide}}
}