ahczhg/ernie-4.5-0.3b-aegis-safety-lora

ERNIE-4.5-0.3B Fine-tuned on Aegis AI Content Safety

Model Description

This is a fine-tuned version of Baidu's ERNIE-4.5-0.3B-Base, optimized for content safety applications. ERNIE (Enhanced Representation through kNowledge IntEgration) is particularly strong in understanding context and knowledge-enhanced language understanding, making it ideal for nuanced safety detection.

This model was fine-tuned using LoRA (Low-Rank Adaptation) on the NVIDIA Aegis AI Content Safety Dataset 2.0, which contains diverse examples of safe and unsafe content across multiple categories.

Model Details

• Base Model: baidu/ERNIE-4.5-0.3B-Base-PT
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Dataset: NVIDIA Aegis AI Content Safety Dataset 2.0
• Training Samples: 2,000 carefully selected samples
• Language: English
• License: Apache 2.0

Capabilities

• Context-aware safety detection
• Knowledge-enhanced content analysis
• Cultural and linguistic sensitivity
• Fine-grained threat classification
• Efficient content safety screening

Intended Use Cases

• Cross-cultural content moderation
• Educational platform safety
• Lightweight mobile applications
• Edge device content filtering
• Multi-lingual safety systems

Training Configuration

LoRA Parameters

• Rank (r): 16
• Alpha: 32
• Dropout: 0.05
• Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Training Hyperparameters

• Learning Rate: 2e-4
• Batch Size: 4 (per device)
• Gradient Accumulation Steps: 4
• Effective Batch Size: 16
• Epochs: 3
• Optimizer: AdamW (8-bit paged)
• LR Scheduler: Cosine with warmup
• Warmup Ratio: 0.1
• FP16 Training: Yes
• Max Sequence Length: 512

Usage

Installation

pip install transformers torch peft

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "ahczhg/ernie-4.5-0.3b-aegis-safety-lora"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Example: Content safety check
prompt = "### Instruction:\nAnalyze this content for safety: 'Your text here'\n\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=128,
        temperature=0.7,
        do_sample=True,
        top_p=0.95
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Advanced Usage with Pipeline

from transformers import pipeline

# Create text generation pipeline
generator = pipeline(
    "text-generation",
    model="ahczhg/ernie-4.5-0.3b-aegis-safety-lora",
    torch_dtype=torch.float16,
    device_map="auto"
)

# Generate safety analysis
result = generator(
    "### Instruction:\nIs this content safe? 'Hello, how are you?'\n\n### Response:\n",
    max_new_tokens=128,
    temperature=0.7,
    do_sample=True
)

print(result[0]['generated_text'])

Evaluation

The model was evaluated on a held-out test set from the Aegis AI Content Safety Dataset. Key metrics include:

• Perplexity on validation set
• Content safety classification accuracy
• False positive/negative rates for harmful content detection

Limitations

• The model is primarily trained on English language content
• Performance may vary on domain-specific or highly technical content
• Should be used as part of a comprehensive content moderation system, not as the sole decision-maker
• May require fine-tuning for specific use cases or content domains
• The model's outputs should be reviewed by human moderators for critical applications

Ethical Considerations

• This model is designed to assist in content safety and moderation tasks
• It should not be used to censor legitimate speech or suppress diverse viewpoints
• Decisions about content moderation should involve human oversight
• The model may reflect biases present in the training data
• Users should implement appropriate safeguards and appeal processes

Training Data

The model was fine-tuned on the NVIDIA Aegis AI Content Safety Dataset 2.0, which includes:

• Diverse examples of safe and unsafe content
• Multiple categories of potentially harmful content
• Balanced representation of safe content
• Real-world scenarios and edge cases

Citation

If you use this model in your research or applications, please cite:

@misc{ernie_0.3b_aegis_safety,
  author = {ahczhg},
  title = {ERNIE-4.5-0.3B Fine-tuned on Aegis AI Content Safety},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/ahczhg/ernie-4.5-0.3b-aegis-safety-lora}},
  note = {Fine-tuned on NVIDIA Aegis AI Content Safety Dataset 2.0}
}

Acknowledgments

• Base model by the original authors: baidu
• Dataset provided by NVIDIA
• Fine-tuning performed using HuggingFace Transformers and PEFT libraries

Contact

For questions, issues, or feedback, please visit the model repository.

Model Card Authors

• ahczhg

Model Card Contact

• https://huggingface.co/ahczhg