---
license: apache-2.0
datasets:
- TeichAI/gpt-5.2-high-reasoning-250x
- TeichAI/gpt-5.1-codex-max-1000x
- TeichAI/claude-4.5-opus-high-reasoning-250x
- TeichAI/claude-sonnet-4.5-high-reasoning-250x
base_model:
- unsloth/gpt-oss-20b
tags:
- gpt_oss
- openai
- unsloth
- conversational
- code
pipeline_tag: text-generation
library_name: transformers
---
# gpt-oss-20b-Coding-Distill
This project uses Unsloth for fine-tuning. All training data is converted to OpenAI Harmony format before training, but there may be cases where the output format doesn't conform to the OpenAI Harmony specification.

## Do you want to use pre-trained model?
You can download pre-trained data from HuggingFace.  
  
**Safetensors repo**: [midorin-Linux/gpt-oss-20b-Coding-Distill](https://huggingface.co/midorin-Linux/gpt-oss-20b-Coding-Distill)  
**GGUF repo**: [midorin-Linux/gpt-oss-20b-Coding-Distill-GGUF](https://huggingface.co/midorin-Linux/gpt-oss-20b-Coding-Distill-GGUF)

## Overview
This project implements a sophisticated multi-phase fine-tuning pipeline for the GPT-OSS-20B model, leveraging conversation data from multiple state-of-the-art AI models to create a balanced, high-performance language model optimized for:

- **Advanced Coding** (via GPT-5.2-codex-max)
- **Complex Reasoning** (via Claude 4.5 Opus and GPT-5.2 high reasoning)
- **Balanced General Intelligence** (via Claude 4.5 Sonnet)

**Why This Approach?**
Traditional fine-tuning often suffers from:

- **Catastrophic forgetting** when training on sequential datasets
- **Imbalanced capabilities** from single-source training
- **Style inconsistencies** across different task types