Jackrong/GPT-5-Distill-llama3.2-3B-Instruct

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

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 (Q4KM, 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.

<|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:

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

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."