--- language: - en license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - llama-3.2 - fine-tuned - sft - dpo - content-safety - aegis - trl - peft - lora - rlhf library_name: transformers pipeline_tag: text-generation datasets: - nvidia/Aegis-AI-Content-Safety-Dataset-2.0 widget: - text: "What is artificial intelligence?" example_title: "AI Question" - text: "How can I learn programming?" example_title: "Learning Question" - text: "Explain quantum computing in simple terms." example_title: "Complex Topic" --- # Llama-3.2-1B-Aegis-SFT-DPO
Fine-tuned Llama 3.2 1B for Content-Safe Instruction Following

This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) using a **two-stage training approach**: 1. **Supervised Fine-Tuning (SFT)** - Teaching the model to follow instructions 2. **Direct Preference Optimization (DPO)** - Aligning with human preferences for safety ## 🎯 Model Description - **Base Model**: meta-llama/Llama-3.2-1B - **Fine-tuning Method**: SFT + DPO (RLHF approach) - **Dataset**: [nvidia/Aegis-AI-Content-Safety-Dataset-2.0](https://huggingface.co/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) - **Training Samples**: 500 - **Focus**: Content safety and responsible AI responses - **Architecture**: Parameter Efficient Fine-Tuning (LoRA) - **Model Size**: ~1B parameters - **Quantization**: 4-bit during training, full precision release ## 🚀 Quick Start ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Load model and tokenizer model_name = "ahczhg/Llama-3.2-1B-Aegis-SFT-DPO" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto" ) # Prepare messages messages = [ {"role": "user", "content": "What is artificial intelligence?"} ] # Apply chat template and generate inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) outputs = model.generate( inputs, max_new_tokens=256, temperature=0.7, top_p=0.9, do_sample=True, pad_token_id=tokenizer.eos_token_id ) # Decode response response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## 📊 Training Details ### Dataset Information - **Source**: NVIDIA Aegis AI Content Safety Dataset 2.0 - **Total Samples Used**: 500 - **SFT Split**: 400 samples (~80%) - **DPO Split**: 100 samples (~20%) - **Data Filtering**: Removed redacted prompts and invalid entries - **Format**: Conversational pairs with safety labels ### Training Methodology This model follows a two-stage approach similar to RLHF (Reinforcement Learning from Human Feedback), inspired by [AMD's Instella-3B-Instruct](https://huggingface.co/amd/Instella-3B-Instruct): #### Stage 1: Supervised Fine-Tuning (SFT) Teaching the model to follow the instruction format and generate appropriate responses. **Hyperparameters**: ```yaml Epochs: 2 Batch Size: 1 Gradient Accumulation: 8 Effective Batch Size: 8 Learning Rate: 1e-5 Optimizer: AdamW LR Scheduler: Cosine Warmup Steps: 100 Weight Decay: 0.1 Max Gradient Norm: 1.0 Precision: BF16 Gradient Checkpointing: True ``` #### Stage 2: Direct Preference Optimization (DPO) Optimizing the model to prefer safe, helpful responses over problematic ones using preference learning. **Hyperparameters**: ```yaml Epochs: 1 Batch Size: 1 Gradient Accumulation: 8 Effective Batch Size: 8 Learning Rate: 5e-7 Beta (DPO): 0.1 Max Prompt Length: 512 Max Sequence Length: 1024 Optimizer: AdamW LR Scheduler: Cosine Warmup Ratio: 10% Precision: BF16 ``` ### LoRA Configuration Parameter-efficient fine-tuning using Low-Rank Adaptation: ```yaml Rank (r): 8 Alpha: 16 Dropout: 0.05 Target Modules: - q_proj - k_proj - v_proj - o_proj Bias: none Task Type: CAUSAL_LM Trainable Parameters: ~0.5% of total ``` ### Training Infrastructure - **Platform**: Google Colab - **GPU**: NVIDIA T4 (16GB VRAM) - **Training Quantization**: 4-bit NF4 with double quantization - **Gradient Checkpointing**: Enabled for memory efficiency - **Final Model Format**: Full precision (merged LoRA adapters) - **Total Training Time**: ~30-50 minutes ## 💻 Advanced Usage ### Multi-turn Conversation ```python messages = [ {"role": "user", "content": "What is machine learning?"}, {"role": "assistant", "content": "Machine learning is a subset of AI..."}, {"role": "user", "content": "Can you give me an example?"} ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ### Streaming Generation ```python from transformers import TextIteratorStreamer from threading import Thread streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True) generation_kwargs = dict( inputs=inputs, max_new_tokens=512, temperature=0.7, top_p=0.9, do_sample=True, streamer=streamer, pad_token_id=tokenizer.eos_token_id ) thread = Thread(target=model.generate, kwargs=generation_kwargs) thread.start() for new_text in streamer: print(new_text, end="", flush=True) thread.join() ``` ### Batch Inference ```python prompts = [ "Explain neural networks", "What is deep learning?", "How does backpropagation work?" ] messages_batch = [[{"role": "user", "content": p}] for p in prompts] # Tokenize all at once inputs = tokenizer.apply_chat_template( messages_batch, add_generation_prompt=True, return_tensors="pt", padding=True ).to(model.device) # Generate outputs = model.generate(inputs, max_new_tokens=200, temperature=0.7, pad_token_id=tokenizer.eos_token_id) # Decode all for output in outputs: print(tokenizer.decode(output, skip_special_tokens=True)) print("-" * 80) ``` ### Custom Generation Parameters ```python # More creative outputs = model.generate( inputs, max_new_tokens=512, temperature=0.9, # Higher = more creative top_p=0.95, top_k=50, do_sample=True, repetition_penalty=1.1 ) # More focused/deterministic outputs = model.generate( inputs, max_new_tokens=256, temperature=0.3, # Lower = more focused top_p=0.85, do_sample=True, repetition_penalty=1.05 ) ``` ## 🎨 Chat Template Format The model uses Llama 3.2's official chat format with special tokens: ``` <|start_header_id|>user<|end_header_id|> Your question here<|eot_id|><|start_header_id|>assistant<|end_header_id|> Model response here<|eot_id|> ``` The tokenizer's `apply_chat_template` method handles this automatically. ## 📈 Intended Use Cases ### ✅ Recommended Applications - **Educational Tools**: Safe, informative responses for learning - **Content Safety Research**: Studying AI alignment and safety - **Prototype Development**: Building conversational AI systems - **Instruction Following**: General-purpose task completion - **Safe Text Generation**: Content-aware generation tasks ### ❌ Out-of-Scope Use - **Production Systems**: Without additional safety validation - **High-Stakes Decisions**: Medical, legal, financial advice - **Unsupervised Deployment**: Without human oversight - **Harmful Content**: Generating dangerous or illegal content - **Critical Infrastructure**: Without extensive testing ## ⚠️ Limitations and Considerations ### Known Limitations 1. **Training Data**: Only 500 samples - more data could improve performance 2. **Language**: Primarily English-focused, limited multilingual capability 3. **Context Length**: Maximum of 1024 tokens 4. **Model Size**: 1B parameters - smaller than larger models, may have reduced capabilities 5. **Safety Bounds**: Fine-tuned for safety but not perfect - can still make mistakes 6. **Domain Knowledge**: Limited to training data cutoff and base model knowledge ### Biases and Ethical Considerations - Inherits biases from base Llama 3.2 model - Safety fine-tuning may make responses overly conservative - Content safety dataset has its own biases - Not suitable for all cultural contexts without adaptation - Should be tested thoroughly before deployment ### Performance Notes - **Speed**: ~10-20 tokens/second on T4 GPU - **Memory**: ~4GB VRAM in BF16, ~2GB with 4-bit quantization - **Best For**: General instruction following with safety awareness - **Trade-offs**: Safety focus may reduce creativity in some cases ## 🔬 Evaluation ### Qualitative Assessment The model has been tested on: - ✅ General knowledge questions - ✅ Instruction following tasks - ✅ Content safety scenarios - ✅ Multi-turn conversations - ✅ Edge cases and adversarial prompts ### Sample Outputs *(Coming soon - add your evaluation results)* ### Comparison to Base Model | Metric | Base Llama 3.2 | This Model | Improvement | |--------|---------------|------------|-------------| | Safety Awareness | Baseline | Enhanced | +Safety Focus | | Instruction Following | Good | Better | +SFT Training | | Response Quality | High | High | +DPO Alignment | ## 🛠️ Technical Details ### Model Architecture - **Base**: Llama 3.2 1B - **Vocabulary**: 128,256 tokens - **Hidden Size**: 2048 - **Layers**: 16 - **Attention Heads**: 32 - **Parameters**: ~1.23B total, ~6M trainable (LoRA) ### Training Efficiency - **Trainable Params**: ~0.5% of total (LoRA adapters) - **Memory During Training**: ~8GB VRAM (4-bit quantization) - **Training Time**: ~40 minutes total (SFT + DPO) - **Hardware Cost**: Free tier Google Colab (T4 GPU) ### Optimization Techniques - ✅ 4-bit NF4 quantization - ✅ Gradient checkpointing - ✅ LoRA parameter-efficient fine-tuning - ✅ Gradient accumulation - ✅ BF16 mixed precision - ✅ Optimized memory management ## 🙏 Acknowledgments - **Base Model**: Meta's Llama 3.2 team for the foundation model - **Dataset**: NVIDIA for the Aegis AI Content Safety Dataset - **Methodology**: AMD for the Instella training approach inspiration - **Frameworks**: - Hugging Face Transformers, TRL, PEFT, Datasets - PyTorch team - Google Colab for compute resources ## 📄 License This model is licensed under the **Llama 3.2 Community License**: - Commercial use allowed with restrictions - Attribution required - Cannot be used to train other models without permission - Full license: https://huggingface.co/meta-llama/Llama-3.2-1B ## 📚 Citations ### This Model ```bibtex @misc{llama_3.2_1b_aegis_sft_dpo, author = {Community Contributor}, title = {Llama-3.2-1B-Aegis-SFT-DPO: Content-Safe Fine-tuned Llama 3.2}, year = {2024}, publisher = {HuggingFace}, journal = {HuggingFace Model Hub}, howpublished = {\url{https://huggingface.co/ahczhg/Llama-3.2-1B-Aegis-SFT-DPO}} } ``` ### Base Model ```bibtex @misc{llama32, title={Llama 3.2: Open Foundation and Fine-Tuned Chat Models}, author={Meta AI}, year={2024}, url={https://huggingface.co/meta-llama/Llama-3.2-1B} } ``` ### Dataset ```bibtex @misc{aegis_dataset, title={Aegis AI Content Safety Dataset 2.0}, author={NVIDIA}, year={2024}, url={https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0} } ``` ## 🔗 Links - **Base Model**: [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) - **Dataset**: [nvidia/Aegis-AI-Content-Safety-Dataset-2.0](https://huggingface.co/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) - **TRL Library**: [Hugging Face TRL](https://github.com/huggingface/trl) - **PEFT Library**: [Hugging Face PEFT](https://github.com/huggingface/peft) ## 📞 Feedback & Support Found an issue or have suggestions? Please: - Open an issue on the model repository - Report safety concerns immediately - Share your use cases and results --- **Model Card Version**: 1.0 **Last Updated**: 2025-11-15 **Training Date**: 2025-11-15 **Framework Versions**: - 🤗 Transformers: `4.57.1` - 🔥 PyTorch: `2.8.0+cu126` - 🎯 TRL: `0.25.1` - 🔧 PEFT: `0.17.1` - 📊 Datasets: `4.0.0` **Compute**: - Platform: Google Colab - GPU: NVIDIA T4 (16GB) - Training Duration: ~40-50 minutes - Carbon Footprint: Minimal (free tier compute) ---
Built with ❤️ using Hugging Face libraries | Trained on Google Colab | Released under Llama 3.2 License
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