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Model: ahczhg/Llama-3.2-1B-Aegis-SFT-DPO
Source: Original Platform

.gitattributes

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README.md

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+---
+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
+
+<div align="center">
+  <strong>Fine-tuned Llama 3.2 1B for Content-Safe Instruction Following</strong>
+</div>
+
+<br>
+
+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)
+
+---
+
+<div align="center">
+  <sub>Built with ❤️ using Hugging Face libraries | Trained on Google Colab | Released under Llama 3.2 License</sub>
+</div>
+
+[![Support me on Ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/ahczhg)
+

chat_template.jinja

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+{% for message in messages %}{% if message['role'] == 'user' %}{{ '<|start_header_id|>user<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }}{% elif message['role'] == 'assistant' %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128001,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.57.1"
+}

model.safetensors

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+oid sha256:7d4a8e8fc3ba2debf7aba9f0656616fd3aeca64ec9026f4eb8d3e5f30835ea98
+size 2471645608

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|end_of_text|>"
+}