Instructions to use KS150/testDPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KS150/testDPO with Transformers:

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

pipe = pipeline("text-generation", model="KS150/testDPO")
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("KS150/testDPO")
model = AutoModelForCausalLM.from_pretrained("KS150/testDPO", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KS150/testDPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KS150/testDPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KS150/testDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KS150/testDPO

SGLang

How to use KS150/testDPO 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 "KS150/testDPO" \
    --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": "KS150/testDPO",
		"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 "KS150/testDPO" \
        --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": "KS150/testDPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use KS150/testDPO with Docker Model Runner:
```
docker model run hf.co/KS150/testDPO
```

testDPO / README.md

KS150

Upload README.md with huggingface_hub

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

	# qwen3-4b-dpo-qwen-cot-merged

	This model is a fine-tuned version of Qwen/Qwen3-4B-Instruct-2507 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: Qwen/Qwen3-4B-Instruct-2507
	- Method: DPO (Direct Preference Optimization)
	- Epochs: 3
	- Learning rate: 7e-04
	- Beta: 0.1
	- Max sequence length: 256
	- LoRA Config: r=8, 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]))

	```

	## 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.