sft-base4-dpo-e2-qwen-cot-merged

＜【課題】sft-base4-dpo-e2-qwen-cot-merged＞

This model is a fine-tuned version of Qwen/Qwen3-4B-Instruct-2507 using Direct Preference Optimization (DPO) via the Unsloth library.

This repository contains the full-merged 16-bit weights. No adapter loading is required.

Training Objective

This repository provides a LoRA (Low-Rank Adaptation) adapter for Qwen/Qwen3-4B-Instruct-2507. This model has been specifically fine-tuned through a multi-stage process, starting from SFT (Supervised Fine-Tuning) followed by DPO (Direct Preference Optimization) to enhance its reasoning and alignment capabilities.

Training Configuration

• Base model: Qwen/Qwen3-4B-Instruct-2507
• SFT Phase
  • Firest, the base model was trained using the u-10bei/structured_data_with_cot_dataset_512_v5 to acquire domain-specific knowledge. (See: Hi-Satoh/sft-base4-qwen-adapter)
• DPO Phase
• Method: DPO (Direct Preference Optimization)
• Epochs: 2
• Learning rate: 1e-07
• Beta: 0.1
• Max sequence length: 2048
• LoRA Config: r=8, alpha=16 (merged into base)

Usage

Since this is a merged model, you can use it directly with transformers.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Hi-Satoh/sft-base4-dpo-e2-qwen-cot-merged"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Test inference
prompt = "Your question here"
inputs = tokenizer.apply_chat_template([{"role": "user", "content": prompt}], tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Sources & License (IMPORTANT)

• Training Data: [u-10bei/dpo-dataset-qwen-cot]
• License: MIT License. (As per dataset terms).
• Compliance: Users must follow the original base model's license terms.