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"
)

prompt = "Your question here"
inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
)
inputs = inputs.to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))

Sources & License (IMPORTANT)

Training Data: [u-10bei/dpo-dataset-qwen-cot]
Compliance: Users must follow the original base model's license terms.

Ran21050/qwen3-4b-dpo-qwen-cot-merged-lora-param-tuned

qwen3-4b-dpo-qwen-cot-merged-lora-param-tuned

Training Configuration

Usage

Sources & License (IMPORTANT)

Ran21050/
qwen3-4b-dpo-qwen-cot-merged-lora-param-tuned