jackrong/gpt-5-distill-llama3.1-8b-instruct-gguf overview

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    "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"
    ],
    "frontmatter": {
      "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"
      ]
    },
    "hero_image_url": "https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/PNNVeEd1bKdL3F7oXCj5M.png",
    "summary": "!Unsloth !Llama-3 !Distillation",
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    "readme_markdown": "---\nbase_model: meta-llama/Llama-3.1-8B-Instruct\nlibrary_name: transformers\nmodel_name: GPT-5-Distill-llama3.1-8B-Instruct\ntags:\n- unsloth\n- llama-3\n- llama\n- text-generation\n- distillation\n- gpt-5\nlicense: llama3.1\nlanguage:\n- en\n- zh\n---\n\n# GPT-5-Distill-llama3.1-8B-Instruct\n\n![Unsloth](https://img.shields.io/badge/Unsloth-Fine--Tuning-blue?style=flat&logo=unsloth)\n![Llama-3](https://img.shields.io/badge/Model-Llama--3.1-green?style=flat)\n![Distillation](https://img.shields.io/badge/Technique-Knowledge%20Distillation-orange?style=flat)\n\n## Model Summary\n\n\n<img src=\"https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/PNNVeEd1bKdL3F7oXCj5M.png\" width=\"800\" />\n\n**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.\n\nThis 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.\n\n* **Base Model:** `meta-llama/Llama-3.1-8B-Instruct`\n* **Architecture:** Llama 3.1 (8B parameters)\n* **Language:** English (Primary)\n* **Context Window:** 32,768 tokens\n* **Fine-tuning Framework:** [Unsloth](https://github.com/unslothai/unsloth) (QLoRA)\n\n## ✨ Key Advantages of GPT-5 Distillation\n\nThis model represents a shift towards **\"Super-Knowledge Distillation\"**, where a smaller, efficient student model learns from a significantly more capable teacher.\n\n* **🚀 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.\n* **⚡ 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.\n* **💎 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.\n* **🎯 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.\n\n## 📚 Training Data\n\nThe model was trained on a high-quality blend of two datasets, totaling **163,896 samples**:\n\n1.  **Chat-GPT-5-Chat-Response (160k samples)**\n    * Filtered specifically for normal entries to ensure high-quality, safe, and coherent responses.\n    * This dataset serves as the primary distillation source, aiming to mimic the response patterns of advanced large language models.\n2.  **ShareGPT-Qwen3-235B-A22B-Instuct-2507 (3.9k samples)**\n    * \"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.\n\nAll data was formatted using the standard **Llama-3 Chat Template**.\n\n## ⚙️ Training Details\n\n* **Hardware:** NVIDIA H100\n* **Sequence Length:** 32,768 tokens (Long Context Support)\n* **Batch Size:** 4 per device (Effective Batch Size: 32 via Gradient Accumulation)\n* **Learning Rate:** 2e-5\n* **Scheduler:** Linear\n* **Optimizer:** AdamW 8-bit\n* **LoRA Rank (r):** 32\n* **LoRA Alpha:** 32\n* **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`\n\n## 🛡️ License & Limitations\n\n* **License:** This model is subject to the **Llama 3.1 Community License**.\n* **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.",
    "related_quantizations": []
  },
  "tags": [
    "transformers",
    "gguf",
    "unsloth",
    "llama-3",
    "llama",
    "text-generation",
    "distillation",
    "gpt-5",
    "en",
    "zh",
    "base_model:meta-llama/Llama-3.1-8B-Instruct",
    "base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
    "license:llama3.1",
    "endpoints_compatible",
    "region:us",
    "conversational"
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  "likes": 0,
  "downloads": 286,
  "gated": false,
  "private": false,
  "last_modified": "2025-11-29T02:44:07.000Z",
  "created_at": "2025-11-28T14:25:46.000Z",
  "pipeline_tag": "text-generation",
  "library_name": "transformers"
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  "createdAt": "2025-11-28T14:25:46.000Z",
  "lastModified": "2025-11-29T02:44:07.000Z",
  "author": "Jackrong",
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