gemma-2b_hh_harmful

gemma-2b Fine-tuned on sft-harm-data

This model is a fine-tuned version of google/gemma-2b on the activeDap/sft-harm-data dataset.

Training Results

Training Statistics

Training Configuration

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "activeDap/gemma-2b_sft-harm-data"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Format input with prompt template
prompt = "What is machine learning?\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate response
outputs = model.generate(**inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Framework

• Library: Transformers + TRL
• Training Type: Supervised Fine-Tuning (SFT)
• Format: Prompt-completion with Assistant-only loss

Citation

If you use this model, please cite the original base model and dataset:

@misc{ultrafeedback2023,
      title={UltraFeedback: Boosting Language Models with High-quality Feedback},
      author={Ganqu Cui and Lifan Yuan and Ning Ding and others},
      year={2023},
      eprint={2310.01377},
      archivePrefix={arXiv}
}