ikedabent/dpo-qwen-cot-merged-v1

qwen3-4b-structured-output-lora-dpo-qwen-cot-merged

★説明に追加

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

This model is a fine-tuned version of Qwen/Qwen3-4B-Instruct-2507 that starts from an SFT LoRA adapter and is further optimized using Direct Preference Optimization (DPO) via the Unsloth library.

• SFT adapter (starting point): ikedabent/qwen3-4b-structured-output-lora-b2
• SFT dataset: u-10bei/structured_data_with_cot_dataset_512_v2
• DPO dataset: u-10bei/dpo-dataset-qwen-cot

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

Training Objective

★This model has been optimized using DPO to align its responses with preferred outputs, focusing on improving reasoning (Chain-of-Thought) and structured response quality based on the provided preference dataset.

This model has been optimized using DPO to prefer more format-consistent structured outputs (e.g., JSON/YAML/TOML/XML/CSV) based on the provided preference dataset.

★Initialization追加, LoRA Config変更

Training Configuration

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Method: DPO (Direct Preference Optimization)
• Initialization: Start from an SFT LoRA adapter, then run DPO
• Epochs: 1
• Learning rate: 1e-07
• Beta: 0.1
• Max sequence length: 1024

★- LoRA Config: r=8, alpha=16 (merged into base)

• LoRA Config: Inherited from the SFT adapter (see adapter_config.json), and 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 = "your_id/your-repo-name"

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)

★sftを追加

* Training Data: [u-10bei/dpo-dataset-qwen-cot]

• Training Data: [u-10bei/structured_data_with_cot_dataset_512_v2], [u-10bei/dpo-dataset-qwen-cot]
• License: MIT License. (As per dataset terms).
• Compliance: Users must follow the original base model's license terms.