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Model: Jackrong/GPT-5-Distill-Qwen3-4B-Instruct Source: Original Platform
2026-05-19 23:06:48 +08:00
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+---
+tags:
+- gguf
+- llama.cpp
+license: apache-2.0
+datasets:
+- Jackrong/ShareGPT-Qwen3-235B-A22B-Instuct-2507
+language:
+- en
+- zh
+base_model:
+- Qwen/Qwen3-4B-Instruct-2507
+---
+
+
+# GPT-5-Distill-Qwen3-4B-Instruct-2507
+
+![Base Model](https://img.shields.io/badge/Base_Model-Qwen3--4B--Instruct-0088CC?style=flat)
+![Distillation](https://img.shields.io/badge/Distillation-GPT--5_Responses-8A2BE2?style=flat)
+![Language](https://img.shields.io/badge/Language-English_%7C_Chinese-blue?style=flat)
+![Context](https://img.shields.io/badge/Context-32K_Tokens-success?style=flat)
+![Format](https://img.shields.io/badge/Format-GGUF_%7C_llama.cpp-yellow?style=flat)
+![License](https://img.shields.io/badge/License-Apache_2.0-green?style=flat)
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/sk5gVFD15S0UNMek3gU0o.png" width="800"/>
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/vGzi5hSHJJ72ysJuM5EAv.png" width="800"/>
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/j39PSDVoQmK4EI9pLANpa.png" width="800"/>
+
+**Model Type**: Instruction-tuned conversational LLM  
+  Supports LoRA adapters and full-finetuned models for inference
+- **Base Model**: `Qwen/Qwen3-4B-Instruct-2507`
+- **Parameters**:  4B 
+- **Training Method**:
+  - Supervised Fine-Tuning (SFT) on ShareGPT data
+  - Knowledge distillation from LMSYS GPT-5 responses
+- **Supported Languages**: Chinese, English, mixed inputs/outputs
+- **Max Context Length**: Up to **32K tokens** (`max_seq_length = 32768`)
+
+This model is trained on ShareGPT-Qwen3 instruction datasets and distilled toward the conversational style and quality of GPT-5. It aims to achieve high-quality, natural-sounding dialogues with low computational overhead—perfect for lightweight applications without sacrificing responsiveness.
+
+---
+
+## 2. Intended Use Cases
+
+### ✅ Recommended:
+
+- Casual chat in Chinese/English
+- General knowledge explanations & reasoning guidance
+- Code suggestions and simple debugging tips
+- Writing assistance: editing, summarizing, rewriting
+- Role-playing conversations (with well-designed prompts)
+
+### ⚠️ Not Suitable For:
+
+- High-risk decision-making:
+  - Medical diagnosis, mental health support
+  - Legal advice, financial investment recommendations
+- Real-time factual tasks (e.g., news, stock updates)
+- Authoritative judgment on sensitive topics
+
+> **Note**: Outputs are for reference only and not intended as the sole basis for critical decisions.
+
+---
+
+## 3. Training Data & Distillation Process
+
+### Key Datasets:
+
+#### (1) ds1: ShareGPT-Qwen3 Instruction Dataset  
+- Source: `Jackrong/ShareGPT-Qwen3-235B-A22B-Instuct-2507`  
+- Purpose:
+  - Provides diverse instruction-response pairs
+  - Supports multi-turn dialogues and context awareness
+- Processing:
+  - Cleaned for quality and relevance
+  - Standardized into `instruction`, `input`, `output` format
+
+#### (2) ds2: LMSYS GPT-5 Teacher Response Data  
+- Source: `ytz20/LMSYS-Chat-GPT-5-Chat-Response`  
+- Filtering:
+  - Only kept samples with `flaw == "normal"`
+  - Removed hallucinations and inconsistent responses
+- Purpose:
+  - Distillation target for conversational quality
+  - Enhances clarity, coherence, and fluency
+
+### Training Flow:
+
+1. Prepare unified Chat-formatted dataset
+2. Fine-tune base Qwen3-4B-Instruct-2507 via SFT
+3. Conduct knowledge distillation using GPT-5's normal responses as teacher outputs
+4. Balance style imitation with semantic fidelity to ensure robustness
+
+> ⚖️ **Note**: This work is based on publicly available, non-sensitive datasets and uses them responsibly under fair use principles.
+
+---
+
+## 4. Key Features Summary
+
+| Feature | Description |
+|--------|-------------|
+| **Lightweight** | ~4B parameter model – fast inference, low resource usage |
+| **Distillation-Style Responses** | Mimics GPT-5’s conversational fluency and helpfulness |
+| **Highly Conversational** | Excellent for chatbot-style interactions with rich dialogue flow |
+| **Multilingual Ready** | Seamless support for Chinese and English |
+
+---
+
+## 5. Acknowledgements
+
+We thank:
+- LMSYS team for sharing GPT-5 response data
+- Jackrong for the ShareGPT-Qwen3 dataset
+- Qwen team for releasing `Qwen3-4B-Instruct`
+
+This project is an open research effort aimed at making high-quality conversational AI accessible with smaller models.
+
+---