Parameter	Value
Base Model	meta-llama/Llama-3.2-3B
Dataset	activeDap/sft-harm-data
Number of Epochs	1.0
Per Device Batch Size	16
Gradient Accumulation Steps	1
Total Batch Size	64 (4 GPUs)
Learning Rate	2e-05
LR Scheduler	cosine
Warmup Ratio	0.1
Max Sequence Length	512
Optimizer	adamw_torch_fused
Mixed Precision	BF16

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "activeDap/Llama-3.2-3B_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:

bibtex

@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}
}

activeDap/Llama-3.2-3B_hh_harmful

Llama-3.2-3B Fine-tuned on sft-harm-data

Training Results

Training Statistics

Training Configuration

Usage

Training Framework

Citation

activeDap/
Llama-3.2-3B_hh_harmful