base_model, datasets, language, license, library_name, pipeline_tag, tags

base_model	datasets	language	license	library_name	pipeline_tag	tags
motobrew/qwen3-adv-comp-v34	motobrew/alf-dpo-from-top-alf93-v0	en	apache-2.0	transformers	text-generation	dpo unsloth qwen alignment

qwen-dpo-v3

This model is a fine-tuned version of motobrew/qwen3-adv-comp-v34 using Direct Preference Optimization (DPO) via the Unsloth library.

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: motobrew/qwen3-adv-comp-v34
• Method: DPO (Direct Preference Optimization)
• Epochs: 1
• Learning rate: 2e-06
• Beta: 0.02
• Max sequence length: 1024

Usage

You can use this model directly with transformers.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "motobrew/qwen-dpo-v3"

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: [motobrew/alf-dpo-from-top-alf93-v0]
• License: MIT License. (As per dataset terms).
• Compliance: Users must follow the original base model's license terms.