metadata
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
jm03
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: 1
- Learning rate: 1e-07
- 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(
prompt,
return_tensors="pt"
).to("cuda")
outputs = model.generate(
**inputs,
max_new_tokens=512
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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.