---
tags:
- gguf
- llama
- 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
---

# GPT-5-Distill-llama3.2-3B-Instruct

![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)

**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

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."

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```