Instructions to use Honejudo1234/dpo-qwen-cot-merged_exp08a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Honejudo1234/dpo-qwen-cot-merged_exp08a with Transformers:

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

pipe = pipeline("text-generation", model="Honejudo1234/dpo-qwen-cot-merged_exp08a")
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_exp08a")
model = AutoModelForCausalLM.from_pretrained("Honejudo1234/dpo-qwen-cot-merged_exp08a", 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_exp08a 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_exp08a"
# 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_exp08a",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Honejudo1234/dpo-qwen-cot-merged_exp08a

SGLang

How to use Honejudo1234/dpo-qwen-cot-merged_exp08a 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_exp08a" \
    --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_exp08a",
		"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_exp08a" \
        --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_exp08a",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Honejudo1234/dpo-qwen-cot-merged_exp08a with Docker Model Runner:
```
docker model run hf.co/Honejudo1234/dpo-qwen-cot-merged_exp08a
```

Honejudo1234 commited on Feb 5

Commit

40f22cc

verified ·

1 Parent(s): 1ccf499

Upload README.md with huggingface_hub

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README.md +82 -13

README.md CHANGED Viewed

@@ -1,21 +1,90 @@
 ---
-base_model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen3
-license: apache-2.0
 language:
 - en
 ---
-# Uploaded finetuned  model
-- **Developed by:** Honejudo1234
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
-This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
+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
+### 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** | 8 |
+| **Effective Batch Size** | 16 |
+> **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`.
+```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.