Instructions to use makotonlo/LLM2026_DPO_finalv5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use makotonlo/LLM2026_DPO_finalv5 with Transformers:

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

pipe = pipeline("text-generation", model="makotonlo/LLM2026_DPO_finalv5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("makotonlo/LLM2026_DPO_finalv5", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use makotonlo/LLM2026_DPO_finalv5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "makotonlo/LLM2026_DPO_finalv5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "makotonlo/LLM2026_DPO_finalv5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/makotonlo/LLM2026_DPO_finalv5

SGLang

How to use makotonlo/LLM2026_DPO_finalv5 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 "makotonlo/LLM2026_DPO_finalv5" \
    --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": "makotonlo/LLM2026_DPO_finalv5",
		"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 "makotonlo/LLM2026_DPO_finalv5" \
        --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": "makotonlo/LLM2026_DPO_finalv5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use makotonlo/LLM2026_DPO_finalv5 with Docker Model Runner:
```
docker model run hf.co/makotonlo/LLM2026_DPO_finalv5
```

＜LLM2026_DPO_finalv5＞

This model is a fine-tuned version of unsloth/Qwen2.5-7B-Instruct-bnb-4bit using Direct Preference Optimization (DPO) via the Unsloth library.

This repository contains LoRA adapter weights only. The base model must be loaded separately.

Training Objective

This repository contains LoRA adapter weights only. The base model must be loaded separately using the provided inference code.

Training Configuration

Base model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
Method: DPO (Direct Preference Optimization)
Epochs: 1
Learning rate: 1e-06
Beta: 0.05
Max sequence length: 1024
LoRA Config: r=64, alpha=128 (LoRA adapter)

Usage

This is a LoRA adapter model. Use it with the base model using the PEFT library.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = f"{repo_id}" # 自動的に今回のリポジトリ名が入るように修正

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: -; Downloads are not tracked for this model. How to track

Model tree for makotonlo/LLM2026_DPO_finalv5

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

unsloth/Qwen2.5-7B-Instruct-bnb-4bit

Finetuned

(280)

this model

makotonlo
/

LLM2026_DPO_finalv5