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

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 ---
+language:
+- en
+license: apache-2.0
+base_model: baidu/ERNIE-4.5-0.3B-Base-PT
+tags:
+- content-safety
+- content-moderation
+- safety
+- lora
+- fine-tuned
+- nvidia-aegis
+- text-classification
+datasets:
+- nvidia/Aegis-AI-Content-Safety-Dataset-2.0
+metrics:
+- perplexity
+- accuracy
 library_name: transformers
+pipeline_tag: text-classification
 ---
+# 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](https://huggingface.co/datasets/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](https://huggingface.co/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
+```bash
+pip install transformers torch peft
+```
+### Basic Usage
+```python
+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
+```python
+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](https://huggingface.co/datasets/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:
+```bibtex
+@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](https://huggingface.co/ahczhg/ernie-4.5-0.3b-aegis-safety-lora).
+## Model Card Authors
+- ahczhg
 ## Model Card Contact
+- https://huggingface.co/ahczhg