--- language: en license: apache-2.0 tags: - alignment - llm - helpful - harmless - honest - mixture-of-experts base_model: meta-llama/Llama-2-7b-hf --- # AlignX A fine-tuned LLAMA-2-7B model with Mixture of Calibrated Alignment Experts (MoCaE) for improved helpfulness, harmlessness, and honesty. ## Requirements - Python 3.9+ - GPU with 16GB+ VRAM (or 2× 8GB with `device_map="auto"`) - Access to [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) ## Installation ```bash pip install -r requirements.txt ``` ## Usage ### Python API (recommended) ```python import os import torch from huggingface_hub import snapshot_download from transformers import AutoTokenizer from models.alignx import build_alignx_model from inference import generate # includes built-in safety filter # Download model files from HuggingFace local_dir = snapshot_download("GautamKashyap/AlignX") # Load AlignX model with MoCaE model = build_alignx_model( base_model_name_or_path="meta-llama/Llama-2-7b-hf", finetuned_paths={ ax: os.path.join(local_dir, f"lora_{ax}") for ax in ("helpful", "harmless", "honest") }, task_matrix_paths={ ax: os.path.join(local_dir, "task_vectors", f"T_{ax}.pt") for ax in ("helpful", "harmless", "honest") }, load_in_4bit=True, device_map="auto", ) model.load_mocae(os.path.join(local_dir, "mocae_finetuning_plus_mocae")) model.eval() tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf") # Generate response (safety filter applied automatically) response = generate(model, tokenizer, "Explain the concept of neural networks.") print(response) ``` ### Multiple prompts ```python prompts = [ "What are three tips for better sleep?", "How does photosynthesis work?", "Is the Great Wall of China visible from space?", ] for p in prompts: print(f"Q: {p}") print(f"A: {generate(model, tokenizer, p)}\n") ``` ### Command line — single prompt ```bash python inference.py --prompt "Explain the concept of neural networks." ``` ### Command line — interactive mode ```bash python inference.py --interactive ``` ## License Apache 2.0. Please also respect the license of the base model.