---
license: apache-2.0
datasets:
- Jackrong/Natural-Reasoning-gpt-oss-120B-S1
- Jackrong/ShareGPT-gpt-oss-120B-reasoning
language:
- en
- zh
base_model:
- Qwen/Qwen3-4B
---
## 📦 Model Card: `Jackrong/gpt-oss-120b-Distill-Qwen3-4B-Thinking`

| Key Property | Value |
|--------------|-------|
| **Model ID** | `Jackrong/gpt-oss-120b-Distill-Qwen3-4B-Thinking` |
| **License** |  apache-2.0 |
| **Author(s)** | Jackrong, gpt‑oss team, Qwen authors |
| **Base Model** | `gpt-oss-120b-high` (complex reasoning dataset distilled) |
| **Target Size** | ~ 4B parameters (`Qwen3‑4B` distilled version) |

---

## 🔍 Overview

A **deeply distilled and fine-tuned variant** of the large‑language model `gpt-oss-120b-high`, optimized for human‑friendly, high‑fidelity reasoning. The model preserves the original’s multi‑step thinking patterns while compressing them onto a lightweight 4B‑parameter backbone (the “Distill‑Qwen3” architecture). Its signature feature is an **explicit point‑by‑point thought chain** that makes intricate logic transparent and easy to follow, ideal for education, technical support, and analytical tasks.

> 💡 *Think of it as the “thinking mode” you’d expect from a massive*

---

## 🛠️ Technical Details

| Aspect | Specification |
|--------|----------------|
| **Source Model** | `gpt-oss‑120b‑high` (complex reasoning dataset distilled) |
| **Distillation Target** | Qwen3‑4B architecture  |
| **Supervised Fine‑Tuning (SFT)** | ~ 30,000 examples drawn from the source’s high‑fidelity reasoning corpus |
| **Training Hardware** | Single NVIDIA H100‑80GB GPU|
| **Max Context Length** | **32 768 tokens** – enables multi‑paragraph, long‑form reasoning without truncation |
| **Reasoning Style** | Default: Bullet‑point “thought chain” output (e.g., `• Step 1 → …\n• Step 2 → …`) |



## 🎯 Recommended Use Cases

| Case | When to use |
|------|--------------|
| **Technical tutorials** | Leverage bullet‑point logic for stepwise code walkthroughs |
| **Complex queries** (e.g., math, engineering) | The model’s deep reasoning helps avoid oversimplified answers |
| **User education** | Clear, scannable outputs aid learning and reduce confusion |
| **Moderation/analysis** | The structured format makes it easier to parse responses programmatically |

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## 📚 Credits & Contributors

- **gpt‑oss team**: Provided the high‑fidelity complex‑reasoning dataset.
- **Qwen3 authors**: Open‑source architecture used as distillation target.
- **Jackrong**: Implemented the final SFT and packaging for Hugging Face Hub.

---