Qwen3-1.7B_hh_harmful/README.md

license, base_model, tags, datasets, language, library_name

license	base_model	tags	datasets	language	library_name
apache-2.0	Qwen/Qwen3-1.7B	generated_from_trainer sft ultrafeedback	activeDap/sft-harm-data	en	transformers

Qwen3-1.7B Fine-tuned on sft-harm-data

This model is a fine-tuned version of Qwen/Qwen3-1.7B on the activeDap/sft-harm-data dataset.

Training Results

Training Statistics

Metric	Value
Total Steps	35
Final Training Loss	2.2961
Min Training Loss	2.2961
Training Runtime	14.67 seconds
Samples/Second	150.44

Training Configuration

Parameter	Value
Base Model	Qwen/Qwen3-1.7B
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

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "activeDap/Qwen3-1.7B_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}
}