fenffef/Qwen-4B-Instruct-2507-Self-correct

Qwen-4B-Instruct-2505-Self-correct

This is a Sherlock-style debiasing model trained using the Self-Correction approach for bias mitigation.

Model Description

• Base Model: Qwen/Qwen3-4B-Instruct-2507
• Training Method: LoRA (Low-Rank Adaptation) with QLoRA (4-bit quantization), then merged
• Task: Bias mitigation and self-correction
• Framework: PyTorch + Transformers + PEFT

Training Details

This model was trained using the Sherlock framework which includes:

1. Stage I (SFT): Supervised fine-tuning on bias correction examples
2. Stage II (Offline): Preference learning with DPO + Self-Correction loss

Key Features

• Self-correction capability for biased reasoning
• Trajectory-level preference learning
• Dynamic β adaptation based on divergence points

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model
model = AutoModelForCausalLM.from_pretrained(
    "fenffef/Qwen-4B-Instruct-2505-Self-correct",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("fenffef/Qwen-4B-Instruct-2505-Self-correct")

# Generate
messages = [
    {"role": "user", "content": "Your prompt here"}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Configuration

Configuration file not found.

Citation

If you use this model, please cite:

@article{sherlock2024,
  title={Sherlock: Self-Correcting Framework for Bias Mitigation},
  author={Your Name},
  year={2024}
}

License

This model is released under the Apache 2.0 license.