Instructions to use Honejudo1234/dpo-qwen-cot-merged_exp09 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Honejudo1234/dpo-qwen-cot-merged_exp09 with Transformers:

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

pipe = pipeline("text-generation", model="Honejudo1234/dpo-qwen-cot-merged_exp09")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Honejudo1234/dpo-qwen-cot-merged_exp09")
model = AutoModelForCausalLM.from_pretrained("Honejudo1234/dpo-qwen-cot-merged_exp09", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Honejudo1234/dpo-qwen-cot-merged_exp09 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Honejudo1234/dpo-qwen-cot-merged_exp09"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Honejudo1234/dpo-qwen-cot-merged_exp09",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Honejudo1234/dpo-qwen-cot-merged_exp09

SGLang

How to use Honejudo1234/dpo-qwen-cot-merged_exp09 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 "Honejudo1234/dpo-qwen-cot-merged_exp09" \
    --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": "Honejudo1234/dpo-qwen-cot-merged_exp09",
		"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 "Honejudo1234/dpo-qwen-cot-merged_exp09" \
        --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": "Honejudo1234/dpo-qwen-cot-merged_exp09",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Honejudo1234/dpo-qwen-cot-merged_exp09 with Docker Model Runner:
```
docker model run hf.co/Honejudo1234/dpo-qwen-cot-merged_exp09
```

qwen3-4b-dpo-qwen-cot-upsampled-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.

Dataset

Training Dataset: Honejudo1234/dpo-dataset-qwen-cot-upsampled

This dataset is an upsampled version of the original u-10bei/dpo-dataset-qwen-cot dataset. The upsampling was performed to address class imbalance and improve model performance on underrepresented categories.

Key details:

Original dataset: u-10bei/dpo-dataset-qwen-cot
Upsampled dataset: Honejudo1234/dpo-dataset-qwen-cot-upsampled
Modification: Only upsampling (no content modification or data augmentation)
Purpose: Balance training data distribution

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

Core Hyperparameters

Parameter	Value
Base Model	Qwen/Qwen3-4B-Instruct-2507
Method	DPO (Direct Preference Optimization)
Learning Rate	5e-07
Beta	0.2
Epochs	1
Max Sequence Length	1024

Batch Size Configuration

Parameter	Value
Per Device Train Batch Size	2
Gradient Accumulation Steps	4
Effective Batch Size	8

Note: The effective batch size is calculated as: Effective Batch Size = per_device_batch_size × gradient_accumulation_steps × num_gpus

LoRA Configuration

Parameter	Value
LoRA Rank (r)	8
LoRA Alpha	16
LoRA Dropout	0
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Status	Merged into base model (16-bit)

Optimizer Settings

Parameter	Value
Optimizer	OptimizerNames.ADAMW_8BIT
Weight Decay	0.01
Warmup Ratio	0.1

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)

Original Dataset: u-10bei/dpo-dataset-qwen-cot
Training Dataset (Upsampled): Honejudo1234/dpo-dataset-qwen-cot-upsampled
License: MIT License. (As per dataset terms).
Compliance: Users must follow the original base model's license terms.

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Honejudo1234/dpo-qwen-cot-merged_exp09

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Qwen/Qwen3-4B-Instruct-2507

Finetuned

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dpo-qwen-cot-merged_exp09