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.

Training Configuration

Base model: KazumaTsuboi/LLM_Competition (derived from Qwen/Qwen3-4B-Instruct-2507)
Method: DPO (Direct Preference Optimization)
Epochs: 1
Learning rate: 2e-07
Beta: 0.05
Max sequence length: 1024
LoRA Config: r=16, alpha=16 (merged into base)

Usage

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

python

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]))

KazumaTsuboi/dpo-qwen-cot-merged_v10

＜qwen3-4b-dpo-qwen-cot-merged＞

Training Objective

Training Configuration

Usage

KazumaTsuboi/
dpo-qwen-cot-merged_v10