120 lines
4.3 KiB
Markdown
120 lines
4.3 KiB
Markdown
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---
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tags:
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- gguf
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- llama.cpp
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license: apache-2.0
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datasets:
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- Jackrong/ShareGPT-Qwen3-235B-A22B-Instuct-2507
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language:
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- en
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- zh
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base_model:
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- Qwen/Qwen3-4B-Instruct-2507
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---
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# GPT-5-Distill-Qwen3-4B-Instruct-2507
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<img src="https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/sk5gVFD15S0UNMek3gU0o.png" width="800"/>
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<img src="https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/vGzi5hSHJJ72ysJuM5EAv.png" width="800"/>
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<img src="https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/j39PSDVoQmK4EI9pLANpa.png" width="800"/>
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**Model Type**: Instruction-tuned conversational LLM
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Supports LoRA adapters and full-finetuned models for inference
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- **Base Model**: `Qwen/Qwen3-4B-Instruct-2507`
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- **Parameters**: 4B
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- **Training Method**:
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- Supervised Fine-Tuning (SFT) on ShareGPT data
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- Knowledge distillation from LMSYS GPT-5 responses
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- **Supported Languages**: Chinese, English, mixed inputs/outputs
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- **Max Context Length**: Up to **32K tokens** (`max_seq_length = 32768`)
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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.
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---
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## 2. Intended Use Cases
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### ✅ Recommended:
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- Casual chat in Chinese/English
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- General knowledge explanations & reasoning guidance
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- Code suggestions and simple debugging tips
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- Writing assistance: editing, summarizing, rewriting
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- Role-playing conversations (with well-designed prompts)
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### ⚠️ Not Suitable For:
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- High-risk decision-making:
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- Medical diagnosis, mental health support
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- Legal advice, financial investment recommendations
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- Real-time factual tasks (e.g., news, stock updates)
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- Authoritative judgment on sensitive topics
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> **Note**: Outputs are for reference only and not intended as the sole basis for critical decisions.
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---
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## 3. Training Data & Distillation Process
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### Key Datasets:
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#### (1) ds1: ShareGPT-Qwen3 Instruction Dataset
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- Source: `Jackrong/ShareGPT-Qwen3-235B-A22B-Instuct-2507`
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- Purpose:
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- Provides diverse instruction-response pairs
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- Supports multi-turn dialogues and context awareness
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- Processing:
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- Cleaned for quality and relevance
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- Standardized into `instruction`, `input`, `output` format
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#### (2) ds2: LMSYS GPT-5 Teacher Response Data
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- Source: `ytz20/LMSYS-Chat-GPT-5-Chat-Response`
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- Filtering:
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- Only kept samples with `flaw == "normal"`
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- Removed hallucinations and inconsistent responses
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- Purpose:
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- Distillation target for conversational quality
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- Enhances clarity, coherence, and fluency
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### Training Flow:
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1. Prepare unified Chat-formatted dataset
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2. Fine-tune base Qwen3-4B-Instruct-2507 via SFT
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3. Conduct knowledge distillation using GPT-5's normal responses as teacher outputs
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4. Balance style imitation with semantic fidelity to ensure robustness
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> ⚖️ **Note**: This work is based on publicly available, non-sensitive datasets and uses them responsibly under fair use principles.
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---
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## 4. Key Features Summary
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| Feature | Description |
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|--------|-------------|
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| **Lightweight** | ~4B parameter model – fast inference, low resource usage |
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| **Distillation-Style Responses** | Mimics GPT-5’s conversational fluency and helpfulness |
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| **Highly Conversational** | Excellent for chatbot-style interactions with rich dialogue flow |
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| **Multilingual Ready** | Seamless support for Chinese and English |
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---
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## 5. Acknowledgements
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We thank:
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- LMSYS team for sharing GPT-5 response data
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- Jackrong for the ShareGPT-Qwen3 dataset
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- Qwen team for releasing `Qwen3-4B-Instruct`
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This project is an open research effort aimed at making high-quality conversational AI accessible with smaller models.
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---
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