---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: GPT-5-Distill-llama3.1-8B-Instruct
tags:
- unsloth
- llama-3
- llama
- text-generation
- distillation
- gpt-5
license: llama3.1
language:
- en
- zh
---

# GPT-5-Distill-llama3.1-8B-Instruct

![Unsloth](https://img.shields.io/badge/Unsloth-Fine--Tuning-blue?style=flat&logo=unsloth)
![Llama-3](https://img.shields.io/badge/Model-Llama--3.1-green?style=flat)
![Distillation](https://img.shields.io/badge/Technique-Knowledge%20Distillation-orange?style=flat)

## Model Summary


<img src="https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/PNNVeEd1bKdL3F7oXCj5M.png" width="800" />

**GPT-5-Distill-llama3.1-8B-Instruct** is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct), designed to distill the capabilities of high-performance models (labeled as GPT-5 in source datasets) into a more efficient 8B parameter footprint.

This model was trained using **Unsloth** on a curated mix of approximately **164,000 high-quality instruction-response pairs**, focusing on complex reasoning and "normal" flaw-level responses.

* **Base Model:** `meta-llama/Llama-3.1-8B-Instruct`
* **Architecture:** Llama 3.1 (8B parameters)
* **Language:** English (Primary)
* **Context Window:** 32,768 tokens
* **Fine-tuning Framework:** [Unsloth](https://github.com/unslothai/unsloth) (QLoRA)

## ✨ Key Advantages of GPT-5 Distillation

This model represents a shift towards **"Super-Knowledge Distillation"**, where a smaller, efficient student model learns from a significantly more capable teacher.

* **🚀 Frontier-Level Reasoning**: By training on dataset samples attributed to GPT-5, the model acquires complex reasoning patterns, nuance, and problem-solving strategies that are typically absent in standard datasets or smaller models.
* **⚡ Efficient Intelligence**: Users can experience high-fidelity, coherent, and detailed responses on consumer hardware (e.g., single GPUs) without the latency, privacy concerns, or cost of querying giant proprietary APIs.
* **💎 High-Purity Signal**: The strict filtering for `flaw == "normal"` ensures the model is fine-tuned only on the highest confidence, error-free responses. This minimizes "hallucination inheritance" and aligns the model with safe, helpful behaviors.
* **🎯 Enhanced Nuance & Tone**: Unlike standard finetunes that often sound robotic, this model mimics the more natural, conversational, and adaptive tone found in next-generation frontier models.

## 📚 Training Data

The model was trained on a high-quality blend of two datasets, totaling **163,896 samples**:

1.  **Chat-GPT-5-Chat-Response (160k samples)**
    * Filtered specifically for normal entries to ensure high-quality, safe, and coherent responses.
    * This dataset serves as the primary distillation source, aiming to mimic the response patterns of advanced large language models.
2.  **ShareGPT-Qwen3-235B-A22B-Instuct-2507 (3.9k samples)**
    * "This dataset consists of approximately **3.9k examples**, with an average of about **5 rounds of dialogue** per scenario, designed to enhance the model’s instruction-following ability and task-completion efficiency.

All data was formatted using the standard **Llama-3 Chat Template**.

## ⚙️ Training Details

* **Hardware:** NVIDIA H100
* **Sequence Length:** 32,768 tokens (Long Context Support)
* **Batch Size:** 4 per device (Effective Batch Size: 32 via Gradient Accumulation)
* **Learning Rate:** 2e-5
* **Scheduler:** Linear
* **Optimizer:** AdamW 8-bit
* **LoRA Rank (r):** 32
* **LoRA Alpha:** 32
* **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`

## 🛡️ License & Limitations

* **License:** This model is subject to the **Llama 3.1 Community License**.
* **Limitations:** While this model is distilled from high-capability sources, it is still an 8B parameter model. It may hallucinate facts or struggle with extremely complex reasoning tasks compared to the original teacher models. The "GPT-5" naming refers to the source dataset labels and does not imply access to unreleased OpenAI weights.