📑 Abstract

An educational Large Language Model (LLM) fine-tuning repository designed for beginners and developers. This project provides detailed theoretical explanations, robust data processing workflows, reproducible training pipelines (including Supervised Fine-Tuning and future Reinforcement Learning implementations), and practical deployment strategies. The full training code for the author's open-source projects is fully accessible within this repository.

🏛️ About This Project

This repository is designed as a "Zero to One" learning platform. Whether you have zero technical background or are an experienced developer, you will find reproducible, end-to-end guides that walk you through the entire lifecycle of large language models. Starting from simply registering a Google account and opening Colab, you will learn how to efficiently adapt models to your specific domain needs.

✨ Key Features & Offerings

💡 A Message to Builders

[!NOTE] "For beginners, hobbyists, and anyone curious about AI: this path is learnable."

The purpose of this document is not only to describe one training run, but also to communicate a broader message: fine-tuning, post-training, and even moderate-scale pretraining are not inaccessible technical rituals. They are engineering practices that can be learned, reproduced, and gradually mastered. With open-source models, public datasets, cloud compute platforms, and an increasingly mature training toolchain, what you often need is simply a Google account, a regular laptop, and sustained curiosity.

As a learner who also started from zero, I understand the uncertainty many newcomers face: environment setup complexity, opaque hyperparameters, and anxiety about compute resources often become the first barrier to entry. This is exactly why optimization toolchains such as Unsloth matter: by improving training efficiency and resource utilization, they substantially lower the practical threshold for large-model fine-tuning, turning what once required expensive hardware and specialized experience into something ordinary developers can attempt and master.

In that sense, we all have the opportunity to stand on the shoulders of giants, understand models, adapt models, and give them new capabilities.

No one starts as an expert. But every expert was once brave enough to begin.

🚀 Upcoming Model Support & Roadmap

In the near future, this repository will continuously expand its support for the latest state-of-the-art open-source model families. The upcoming tutorials and codebases will comprehensively cover both Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL - specifically GRPO) pipelines.

Below is the planned support matrix for upcoming model families:

📓 Interactive Training Notebooks

Below are the interactive Kaggle and Colab notebooks, organized by model architecture. You can run the entire pipeline—from data preparation to training and inference—directly in your browser. All notebooks are available in the train_code repository folder.

🌟 Main Notebooks

🤖 Model Architecture	🛠️ Pipeline	🚀 Quick Setup (1-Click Run)
Qwopus3.5 (27B)	SFT
Qwen3.5 (9B)	SFT
Qwopus3.5 (35B)	SFT
Llama3.2-R1 (3B)	RL (GPRO)

📖 Comprehensive Model Training Guide

For a detailed, step-by-step PDF walkthrough of the entire Qwopus 3.5 fine-tuning process—including environment setup, data preparation, and optimization tips—please refer to our latest guide:

[!TIP] 🔗 Download Complete Guide: Qwopus3-5-27b-Colab_complete_guide_to_llm_finetuning.pdf

🔗 Download Technical Report: Qwopus-GLM-18B-Technical-Report.pdf A concise technical report covering the Qwopus-GLM-18B model design, training rationale, and key implementation details.

📚 High-Fidelity Distillation Datasets

High-quality data is the engine of effective model adaptation. In parallel with our training code, this repository provides access to 24 curated, high-fidelity datasets specifically collected and distilled to enhance model reasoning, coding, and conversational capabilities.

These datasets are primarily distilled from state-of-the-art flagship models (such as DeepSeek-V3.2, Qwen3-235B, GLM-4.7, and GPT-OSS-120B) and follow advanced Chain-of-Thought (CoT) formatting.

Key Dataset Categories Included:

• 🧠 Reasoning & CoT (Chain-of-Thought): Datasets like Jackrong/Qwen3.5-reasoning-700, Jackrong/Natural-Reasoning-gpt-oss-120B-S1, and Jackrong/glm-4.7-multiturn-CoT designed to improve step-by-step logic and deduction.
• 📐 Mathematics & STEM: Specialized data such as DeepSeek-v3.1-reasoner-Distilled-math-samples and focused domain knowledge like Jackrong/Qwen3-235B-A22B-Instruct-2507-Distilled-chat.
• 💻 Code & Algorithms: Collections like Competitive-Programming-python-blend and qwen3-coder-480b-distill-mini to strengthen competitive programming and algorithmic generation.
• 💬 Instruction & Multi-turn Chat: Resources like Jackrong/LogicMind-Chat-Reasoning-SFT-300K, Chinese-Qwen3-235B-Thinking-2507-Distill-100k, and ShareGPT-gpt-oss-120B-reasoning focused on human alignment, IELTS writing feedback, and robust conversational flowing.

All datasets are open-sourced on the HuggingFace Hub. You can also use the included download_datasets.py script to batch download the entire suite for local training.

🌍 Open Source Commitment & Community Impact

Moving forward, the complete training source code for every fine-tuned model I release on Hugging Face will be fully open-sourced in this repository. My goal is to ensure that anyone—regardless of their background or resources—can freely download, execute, and learn from these scripts to build their own AI capabilities.

I am deeply grateful for the community's support. The Qwen3.5 fine-tunes I shared on Hugging Face have recently reached over a million downloads—a quiet reminder of the power of open knowledge. It is my sincere hope that making these full training pipelines publicly available will encourage more developers to start their own fine-tuning journeys.

📝 Citation

If you find this repository helpful in your learning or research, please consider citing it:

@misc{jackrong-llm-finetuning,
  author = {Jackrong},
  title = {Jackrong-llm-finetuning-guide: An Educational LLM Fine-Tuning Pipeline},
  year = {2026},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Jackrong/Jackrong-llm-finetuning-guide}}
}