Instructions to use deepkawamura/qwen3-4b-sft-data1-1-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepkawamura/qwen3-4b-sft-data1-1-dpo with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="deepkawamura/qwen3-4b-sft-data1-1-dpo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deepkawamura/qwen3-4b-sft-data1-1-dpo")
model = AutoModelForCausalLM.from_pretrained("deepkawamura/qwen3-4b-sft-data1-1-dpo", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepkawamura/qwen3-4b-sft-data1-1-dpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepkawamura/qwen3-4b-sft-data1-1-dpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepkawamura/qwen3-4b-sft-data1-1-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deepkawamura/qwen3-4b-sft-data1-1-dpo

SGLang

How to use deepkawamura/qwen3-4b-sft-data1-1-dpo with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "deepkawamura/qwen3-4b-sft-data1-1-dpo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepkawamura/qwen3-4b-sft-data1-1-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "deepkawamura/qwen3-4b-sft-data1-1-dpo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepkawamura/qwen3-4b-sft-data1-1-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use deepkawamura/qwen3-4b-sft-data1-1-dpo with Docker Model Runner:
```
docker model run hf.co/deepkawamura/qwen3-4b-sft-data1-1-dpo
```

qwen3-4b-sft-data1-1-dpo

This model is a fine-tuned version of deepkawamura/qwen3-4b-structured-output-lora 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 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: deepkawamura/qwen3-4b-structured-output-lora
Method: DPO (Direct Preference Optimization)
Epochs: 1
Learning rate: 5e-06
Beta: 0.1
Max sequence length: 1024
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 = "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)

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.

Downloads last month: 15

Safetensors

Model size

4B params

Tensor type

F32

Model tree for deepkawamura/qwen3-4b-sft-data1-1-dpo

Base model

Qwen/Qwen3-4B-Instruct-2507

Adapter

deepkawamura/qwen3-4b-structured-output-lora

Quantized

(1)

this model

deepkawamura
/

qwen3-4b-sft-data1-1-dpo