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Model: Jackrong/GPT-5-Distill-llama3.1-8B-Instruct Source: Original Platform
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README.md
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README.md
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---
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base_model: meta-llama/Llama-3.1-8B-Instruct
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library_name: transformers
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model_name: GPT-5-Distill-llama3.1-8B-Instruct
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tags:
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- unsloth
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- llama-3
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- llama
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- text-generation
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- distillation
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- gpt-5
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license: llama3.1
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language:
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- en
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- zh
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---
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# GPT-5-Distill-llama3.1-8B-Instruct
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## Model Summary
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<img src="https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/PNNVeEd1bKdL3F7oXCj5M.png" width="800" />
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**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.
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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.
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* **Base Model:** `meta-llama/Llama-3.1-8B-Instruct`
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* **Architecture:** Llama 3.1 (8B parameters)
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* **Language:** English (Primary)
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* **Context Window:** 32,768 tokens
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* **Fine-tuning Framework:** [Unsloth](https://github.com/unslothai/unsloth) (QLoRA)
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## ✨ Key Advantages of GPT-5 Distillation
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This model represents a shift towards **"Super-Knowledge Distillation"**, where a smaller, efficient student model learns from a significantly more capable teacher.
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* **🚀 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.
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* **⚡ 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.
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* **💎 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.
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* **🎯 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.
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## 📚 Training Data
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The model was trained on a high-quality blend of two datasets, totaling **163,896 samples**:
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1. **Chat-GPT-5-Chat-Response (160k samples)**
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* Filtered specifically for normal entries to ensure high-quality, safe, and coherent responses.
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* This dataset serves as the primary distillation source, aiming to mimic the response patterns of advanced large language models.
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2. **ShareGPT-Qwen3-235B-A22B-Instuct-2507 (3.9k samples)**
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* "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.
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All data was formatted using the standard **Llama-3 Chat Template**.
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## ⚙️ Training Details
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* **Hardware:** NVIDIA H100
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* **Sequence Length:** 32,768 tokens (Long Context Support)
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* **Batch Size:** 4 per device (Effective Batch Size: 32 via Gradient Accumulation)
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* **Learning Rate:** 2e-5
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* **Scheduler:** Linear
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* **Optimizer:** AdamW 8-bit
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* **LoRA Rank (r):** 32
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* **LoRA Alpha:** 32
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* **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
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## 🛡️ License & Limitations
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* **License:** This model is subject to the **Llama 3.1 Community License**.
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* **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.
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chat_template.jinja
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{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '<|start_header_id|>user<|end_header_id|>
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' + message['content'] | trim + '<|eot_id|>' }}{% elif message['role'] == 'assistant' %}{{ '<|start_header_id|>assistant<|end_header_id|>
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' + message['content'] | trim + '<|eot_id|>' }}{% else %}{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>
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' + message['content'] | trim + '<|eot_id|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>
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' }}{% endif %}
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config.json
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"pad_token_id": 128004,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 8.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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"transformers_version": "4.56.2",
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"unsloth_fixed": true,
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"unsloth_version": "2025.11.4",
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"use_cache": true,
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"vocab_size": 128256
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}
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configuration.json
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"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|finetune_right_pad_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2067
tokenizer_config.json
Normal file
2067
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user