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

base_model	datasets	language	license	library_name	pipeline_tag	tags
motobrew/qwen-dpo-v3	motobrew/alf-dpo-from-top-alf93-v0	en	apache-2.0	transformers	text-generation	dpo unsloth qwen alignment

qwen-dpo-v13

This model is a fine-tuned version of motobrew/qwen-dpo-v3 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/qwen-dpo-v3
• Method: DPO (Direct Preference Optimization)
• Epochs: 1
• Learning rate: 2e-06
• Beta: 0.05
• 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-v13"

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.