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: 1e-05
Beta: 0.1
Max sequence length: 1024

Usage

You can use this model directly with transformers.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "motobrew/qwen-dpo-v11"

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.