base_model, datasets, language, license, library_name, pipeline_tag, tags

base_model	datasets	language	license	library_name	pipeline_tag	tags
unsloth/Qwen3-4B-Instruct-2507	u-10bei/structured_data_with_cot_dataset_512_v2 u-10bei/dpo-dataset-qwen-cot	en	apache-2.0	transformers	text-generation	sft dpo unsloth qwen alignment lora

koguma-ai/sft-dpo-qwen-cot-merged0207_unsloth_03

This model is a fine-tuned version of unsloth/Qwen3-4B-Instruct-2507 using a two-stage training pipeline: SFT (Supervised Fine-Tuning) followed by DPO (Direct Preference Optimization) via the Unsloth library.

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

Training Pipeline

Stage 1: SFT (Supervised Fine-Tuning)

The base model was first fine-tuned with SFT to learn structured output generation and Chain-of-Thought reasoning.

• Dataset: u-10bei/structured_data_with_cot_dataset_512_v2
• LoRA Config: r=64, alpha=128, dropout=0
• Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Learning rate: 1e-6
• Epochs: 1
• Max sequence length: 512
• Loss strategy: Assistant-only loss with CoT masking (Output marker)
• SFT LoRA adapter: koguma-ai/sft-dpo-qwen-cot-merged0207_unsloth_03

Stage 2: DPO (Direct Preference Optimization)

The SFT-trained LoRA was merged into the base model, then a new LoRA adapter was applied for DPO training to further align the model with preferred outputs.

• Dataset: u-10bei/dpo-dataset-qwen-cot
• LoRA Config: r=8, alpha=16, dropout=0
• Learning rate: 1e-07
• Epochs: 1
• Beta: 0.1
• Max sequence length: 1024
• Optimizer: AdamW 8-bit

Usage

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

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "koguma-ai/sft-dpo-qwen-cot-merged0207_unsloth_03"

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

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

• SFT Data: u-10bei/structured_data_with_cot_dataset_512_v2
• DPO Data: u-10bei/dpo-dataset-qwen-cot
• License: Apache 2.0 (as per base model terms)
• Compliance: Users must follow the original base model's license terms.