Jackrong/GPT-5-Distill-Qwen3-4B-Instruct

GPT-5-Distill-Qwen3-4B-Instruct-2507

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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. 1.Prepare unified Chat-formatted dataset
2. 2.Fine-tune base Qwen3-4B-Instruct-2507 via SFT
3. 3.Conduct knowledge distillation using GPT-5's normal responses as teacher outputs
4. 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

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.