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

🤗 On Hugging Faceapache-2.036 GBGGUF✓ HF checksums availableupdated today
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GPT-5-Distill-Qwen3-4B-Instruct-2507

!Base Model

!Distillation

!Language

!Context

!Format

!License

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.