Update README.md

README.md

@@ -1,21 +1,147 @@
 ---
-base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
 tags:
-- text-generation-inference
-- transformers
-- unsloth
+- gguf
 - llama
-license: apache-2.0
+- llama-3.2
+- text-generation
+- conversational
+license: llama3.2
+datasets:
+- Jackrong/ShareGPT-Qwen3-235B-A22B-Instuct-2507
+- ytz20/LMSYS-Chat-GPT-5-Chat-Response
 language:
 - en
+- zh
+base_model:
+- unsloth/Llama-3.2-3B-Instruct
 ---
 
-# Uploaded finetuned  model
+# GPT-5-Distill-llama3.2-3B-Instruct
 
-- **Developed by:** Jackrong
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
+![Llama-3.2](https://img.shields.io/badge/Base_Model-Llama--3.2--3B-blue?style=flat&logo=meta&logoColor=white)
+![Instruct](https://img.shields.io/badge/Type-Instruction--Tuned-green?style=flat)
+![GPT-5](https://img.shields.io/badge/Distillation-GPT--5_Responses-8A2BE2?style=flat)
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+**Model Type**: Instruction-tuned Edge LLM (Llama 3.2 Architecture)
+- **Base Model**: `unsloth/Llama-3.2-3B-Instruct`
+- **Parameters**: ~3.2B (Optimized for Edge/Consumer GPU)
+- **Training Method**:
+  - **SFT (Supervised Fine-Tuning)** using Unsloth & TRL
+  - **Knowledge Distillation**: Trained on GPT-5 responses to mimic superior reasoning and tone
+  - **LoRA Config**: r=32, alpha=32, targeting all linear projections
+- **Max Context Length**: **32K tokens** (`max_seq_length = 32768`)
+- **Quantization**: Native GGUF support (Q4_K_M, Q8_0, FP16) provided
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+This model represents a high-efficiency distillation attempt, combining the lightweight, edge-ready architecture of **Llama-3.2-3B** with the high-quality conversational patterns of **GPT-5**. By filtering for "normal" (flawless) responses from the LMSYS dataset, this model aims to deliver flagship-level instruction following in a 3B parameter package.
+
+---
+
+## 2. Intended Use Cases
+
+### ✅ Recommended:
+- **On-Device Chat**: Perfect for laptops, phones, and low-VRAM GPUs due to small size.
+- **Reasoning & Explanations**: Distilled GPT-5 logic helps in providing clearer answers.
+- **Summarization & Rewriting**: Inherits strong English/Chinese capabilities from the dataset mix.
+- **RAG Applications**: 32K context window allows for processing moderate-sized documents.
+
+### ⚠️ Not Suitable For:
+- **Math/Complex Coding**: While capable, 3B models have limitations compared to 70B+ models in complex logic.
+- **High-Stakes Medical/Legal Advice**: Outputs should always be verified.
+- **Hallucination-Free Tasks**: Small models may still hallucinate facts.
+
+---
+
+## 3. Training Data & Methodology
+
+The model was trained on a curated mix of **~104,000 high-quality samples**:
+
+### (1) ds1: ShareGPT-Qwen3 Instruction Mix (~3,900 samples)
+- **Source**: `Jackrong/ShareGPT-Qwen3-235B-A22B-Instuct-2507`
+- **Role**: Provides diverse, multi-turn instruction following capabilities, enhancing the model's ability to handle complex prompts (English & Chinese mixed).
+
+### (2) ds2: LMSYS GPT-5 Teacher Responses (~100,000 samples)
+- **Source**: `ytz20/LMSYS-Chat-GPT-5-Chat-Response`
+- **Filtering Logic**:
+  - Applied rigorous filtering: `flaw == "normal"` (Removed hallucinations, refusals, and bad formatting).
+  - Only clean, high-quality "Teacher" responses were used for distillation.
+- **Role**: Imparts the "GPT-5" conversational style, politeness, and reasoning structure to the smaller Llama model.
+
+### Training Configuration:
+- **Framework**: Unsloth + Hugging Face TRL
+- **Loss Masking**: `train_on_responses_only` was enabled (Model learns to generate answers, not questions).
+- **Optimizer**: AdamW 8-bit for efficiency.
+- **Precision**: Trained in 4-bit, exported to 16-bit and GGUF.
+
+---
+
+## 4. Prompt Format (Llama 3.2 Standard)
+
+This model uses the standard **Llama 3 / 3.2** prompt template.
+
+```text
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+{Your Prompt Here}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+````
+
+**Python Inference Example:**
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "Jackrong/GPT-5-Distill-llama3.2-3B-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Explain quantum mechanics to a 5-year-old."},
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=512,
+    temperature=0.7,
+    do_sample=True
+)
+
+print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))
+```
+
+-----
+
+## 5\. Key Features Summary
+
+| Feature | Description |
+|--------|-------------|
+| **Super Lightweight** | 3B Parameters. Runs on almost any modern consumer hardware. |
+| **GPT-5 Distilled** | Learned from 100k+ clean GPT-5 outputs for superior tone. |
+| **Long Context** | Supports up to **32k context**, great for long conversations. |
+| **GGUF Ready** | Available in `q4_k_m` (very fast) and `q8_0` quantizations. |
+
+-----
+
+## 6\. Acknowledgements
+
+  - **Unsloth**: For the 2x faster training and 4-bit loading capabilities.
+  - **LMSYS Org**: For providing the GPT-5 response dataset.
+  - **Meta AI**: For the robust Llama-3.2 base model.
+
+This project is an open research effort to bring "Big Model Intelligence" to "Small Model Footprints."
+
+```
+```