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VANTA Research

Independent AI safety research lab specializing in cognitive fit, alignment, and human-AI collaboration

--- # Atom V1 Preview **Atom** is an AI assistant developed by VANTA Research focused on collaborative exploration, curiosity-driven dialogue, and pedagogical reasoning. This preview release represents an early R&D iteration built on the Gemma3 architecture ## Model Description Atom v1 Preview is a fine-tuned language model designed to embody: - **Collaborative Exploration**: Engages users through clarifying questions and co-reasoning - **Analogical Thinking**: Employs metaphors and analogies to explain complex concepts - **Enthusiasm for Discovery**: Celebrates insights and maintains genuine curiosity - **Pedagogical Depth**: Provides detailed, thorough explanations that guide reasoning processes This model was developed as a research prototype to explore personality-driven fine-tuning and human-AI collaboration patterns before scaling to larger architectures. ## Technical Specifications - **Base Model**: google/gemma-3-4b-it - **Fine-tuning Method**: LoRA (Low-Rank Adaptation via PEFT) - **Training Framework**: Transformers, PEFT, TRL - **Quantization**: 4-bit (nf4) during training - **Final Format**: Full precision merged model (FP16) - **Parameters**: ~4B - **Context Length**: 128K tokens - **Vocabulary Size**: 262K tokens ### LoRA Configuration ``` Stage 1 (Personality): r=16, alpha=32, dropout=0.05, 2 epochs Stage 2 (Attribution): r=8, alpha=16, dropout=0.02, 2 epochs Stage 3 (Verbosity): r=4, alpha=8, dropout=0.01, 1 epoch ``` ## Intended Use ### Primary Use Cases - Educational dialogue and concept explanation - Collaborative research assistance - Exploratory reasoning and brainstorming - Pedagogical applications requiring detailed explanations - Research into AI personality and interaction patterns ### Out-of-Scope Uses - Production deployment without further evaluation - High-stakes decision making - Commercial applications (see license) - Critical infrastructure or safety-critical systems - Medical, legal, or financial advice ## Usage This repository includes both PyTorch (safetensors) and GGUF formats: - **PyTorch format**: Use with Transformers for GPU inference - **GGUF format** (`atom-v1-preview-4b.gguf`): Use with llama.cpp or Ollama for efficient CPU/GPU inference ### Loading the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_name = "vanta-research/atom-v1-preview" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True ) ``` ### Inference Example ```python messages = [ {"role": "user", "content": "Explain quantum entanglement like I'm 5"} ] input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt" ).to(model.device) outputs = model.generate( input_ids, max_new_tokens=512, temperature=0.8, top_p=0.9, top_k=40, do_sample=True ) response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True) print(response) ``` ### Using GGUF with llama.cpp or Ollama A quantized GGUF version (`atom-v1-preview-4b.gguf`) is included for efficient CPU/GPU inference: **With llama.cpp:** ```bash ./llama-cli -m atom-v1-preview-4b.gguf -p "Explain quantum entanglement" --temp 0.8 --top-p 0.9 ``` **With Ollama:** ```bash # Create Modelfile cat > Modelfile <