73 lines
1.7 KiB
Markdown
73 lines
1.7 KiB
Markdown
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---
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base_model: Qwen/Qwen3-4B-Instruct-2507
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datasets:
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- u-10bei/dpo-dataset-qwen-cot
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- dpo
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- unsloth
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- qwen
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- alignment
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---
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# ＜【課題】ここは自分で記入して下さい＞
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This model is a fine-tuned version of **Qwen/Qwen3-4B-Instruct-2507** using **Direct Preference Optimization (DPO)** via the **Unsloth** library.
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This repository contains the full-merged 16-bit weights. No adapter loading is required.
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## Training Configuration
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- Base model: Qwen/Qwen3-4B-Instruct-2507
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- Method: DPO (Direct Preference Optimization)
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- Epochs: 1
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- Learning rate: 1e-07
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- Beta: 0.1
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- Max sequence length: 1024
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- LoRA Config: r=8, alpha=16 (merged into base)
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## Usage
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "your_id/your-repo-name"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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prompt = "Your question here"
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messages = [
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{"role": "user", "content": prompt}
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]
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input_ids = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt",
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)
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input_ids = input_ids.to(model.device)
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outputs = model.generate(
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input_ids=input_ids,
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max_new_tokens=512,
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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## Sources & License
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* Training Data: [u-10bei/dpo-dataset-qwen-cot]
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* License: MIT License (as per dataset terms)
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* Compliance: Follow base model license terms
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