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koguma-ai/
sft-dpo-qwen-cot-merged0207_unsloth_02

Name: sft-dpo-qwen-cot-merged0207_unsloth_02
Availability: InStock

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sft-dpo-qwen-cot-merged0207

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/lora_structeval_t_qwen3_4b_unsloth_02

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.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "koguma-ai/sft-dpo-qwen-cot-merged0207_unsloth_02"

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.