Jackrong-llm-finetuning-guide
A guide for fine-tuning large language models like LLaMA3 and Qwen using PyTorch
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Decision brief
Jackrong-llm-finetuning-guide: A targeted instructive resource for those seeking to fine-tune their large language models such as LLaMA3 and Qwen using PyTorch.
Good fit when
- You are specifically working with or planning to work with LLaMA3 or Qwen models, which this guide exclusively supports.
- Your project requires PyTorch for model fine-tuning; the guide focuses on detailing the process within that framework.
Avoid when
- You prefer TensorFlow (or another deep learning framework not covered by Jackrong-llm-finetuning-guide) as your primary environment for developing AI models.
- Your interest lies in general knowledge about LLMs without the specifics of implementation or fine-tuning methodologies.
- Requirements:
- Requires Python environment setup for PyTorch and Jupyter Notebook familiarity.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Steady (43d since push)
- As of today
- Provenance
- Not a fork · Personal account
- As of today
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/R6410418/Jackrong-llm-finetuning-guideSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This repository contains a guide focused on the process of fine-tuning large language models such as LLaMA3 and Qwen with PyTorch. The resource likely includes datasets, deepseek techniques, and machine-learning practices pertinent to improving NLP capabilities.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Aug 24, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 24, 2026)
| Run the Qwopus3.6 27B GSPO tutorial | [Open the GSPO Python tutorial](train_code/Qwopus3.6-27B-GSPO/qwopus3_6_27b_gspo_training.py) |Source link
Tags
README
Jackrong LLM Fine-Tuning Guide
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
🌐 Product Website: Explore Qwopus3.6 models and fine-tuning guides →
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.
📚 Table of Contents
- 🚀 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
🚀 Start Here
| I want to... | Recommended entry |
|---|---|
| Fine-tune my first model in a browser | Open the training recipe catalog |
| Run the Qwopus3.6 27B GSPO tutorial | Open the GSPO Python tutorial |
| Prepare or distill training data | Browse data-processing recipes |
| Find curated reasoning, coding, and conversation datasets | Open the dataset catalog |
| Convert a Qwen model to MTP-enabled GGUF | Open the Qwen MTP GGUF Skill |
| Read full beginner guides and reports | Open the PDF guide library |
| Automate repeatable Codex workflows | Open the Codex Goal templates |
🗺️ Repository Map
| Resource | What you will find | Entry |
|---|---|---|
| 🏋️ Training Recipes | SFT, GRPO, and GSPO notebooks and Python tutorials | Open |
| 🧪 Data Processing | Distillation, preprocessing, filtering, and sampling workflows | Open |
| 🧠 Dataset Catalog | Curated high-fidelity datasets and download helpers | Open |
| ⚙️ Qwen MTP GGUF Skill | Agent-ready MTP extraction, injection, conversion, validation, quantization, and upload pipeline | Open |
| 📘 Guides and Reports | Long-form PDF tutorials and technical reports | Open |
| 🌐 Multilingual Docs | Chinese, Korean, and Japanese landing pages plus documentation indexes | Open |
| 🤖 Codex Goal Templates | Editable goal templates for RL training, MTP GGUF conversion, and repository maintenance | Open |
🏋️ Training Recipes
| 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.
✅ Supported Workflows
| Workflow | Status | Documentation |
|---|---|---|
| SFT with LoRA / QLoRA | ✅ Released | Training recipes |
| GRPO reinforcement learning | ✅ Released | Training recipes |
| GSPO reinforcement learning | ✅ Released | Qwopus3.6 27B GSPO tutorial |
| Dataset distillation and preprocessing | ✅ Released | Data-processing recipes |
| LoRA adapter save and merged 16-bit export | ✅ Released | Training recipes |
| GGUF quantiza |
For agents
This page has a .md twin and JSON over the API.