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Model: Jackrong/GPT-5-Distill-Qwen3-4B-Instruct-GGUF
Source: Original Platform

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Modelfile

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+FROM qwen3-4b-instruct-2507.Q8_0.gguf
+TEMPLATE """
+{{- $lastUserIdx := -1 -}}
+{{- range $idx, $msg := .Messages -}}
+{{- if eq $msg.Role "user" }}{{ $lastUserIdx = $idx }}{{ end -}}
+{{- end }}
+{{- if or .System .Tools }}<|im_start|>system
+{{ if .System }}
+{{ .System }}
+{{- end }}
+{{- if .Tools }}
+
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{{- range .Tools }}
+{"type": "function", "function": {{ .Function }}}
+{{- end }}
+</tools>
+
+For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
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+</tool_call>
+{{- end -}}
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+{{ end }}
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+{{- $last := eq (len (slice $.Messages $i)) 1 -}}
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+{{ else if eq .Role "assistant" }}<|im_start|>assistant
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+{{ end -}}
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+{{ end }}</tool_call>
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+"""

README.md

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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.
+
+---

config.json

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+{
+    "architectures": [
+        "Qwen3ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "torch_dtype": "bfloat16",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 9728,
+    "layer_types": [
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
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+        "full_attention",
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+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 262144,
+    "max_window_layers": 36,
+    "model_type": "qwen3",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 8,
+    "pad_token_id": 151654,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": null,
+    "rope_theta": 5000000,
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "transformers_version": "4.56.2",
+    "unsloth_fixed": true,
+    "unsloth_version": "2025.11.3",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

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