--- license: apache-2.0 language: - en base_model: - allenai/Olmo-3-7B-Instruct base_model_relation: finetune library_name: transformers tags: - conversational-ai - cognitive-architectures - chat - safetensors - persona - text - text-generation - persona-ai - roleplay - cognitive - vanta-research - project-atom - atom - conversational - collaborative-ai - text-generation-inference - collaboration - friendly - educational - learning - ai-research - ai-alignment-research - ai-alignment - ai-behavior-research - ai-persona-research - human-ai-collaboration ---
![vanta_trimmed](https://cdn-uploads.huggingface.co/production/uploads/686c460ba3fc457ad14ab6f8/hcGtMtCIizEZG_OuCvfac.png)

VANTA Research

Independent AI research lab building safe, resilient language models optimized for human-AI collaboration

Website Merch X GitHub

--- # Atom-Olmo3-7B Atom-Olmo3-7B is a specialized language model fine-tuned for collaborative problem-solving and creative exploration. Built on the Olmo-3-7B-Instruct foundation, this model brings thoughtful, structured analysis to complex questions while maintaining an engaging, conversational tone. ## Key Features - **Apache 2.0 License**: Fully open-source with permissive licensing for commercial use - **Collaborative Intelligence**: Trained to ask clarifying questions and explore ideas iteratively - **Structured Thinking**: Provides organized, framework-driven responses for complex topics - **Educational Depth**: Breaks down sophisticated concepts into accessible explanations - **Creative Synthesis**: Combines analytical rigor with imaginative problem-solving ## Model Details - **Base Model**: allenai/Olmo-3-7B-Instruct - **Training Method**: LoRA fine-tuning (r=16, alpha=32) - **Training Data**: Curated dataset focused on collaborative reasoning, ELI5 explanations, lateral thinking, and research synthesis - **Context Length**: 4096 tokens (recommended) - **Parameters**: 7B - **Precision**: FP16 ## Intended Use ### Primary Use Cases - Technical brainstorming and ideation - Educational explanations and concept breakdowns - Research synthesis and literature review - Collaborative problem-solving across domains - Framework development and structured analysis ### Out of Scope This model is not intended for: - Medical diagnosis or treatment recommendations - Legal advice or financial counseling - Real-time factual information (knowledge cutoff applies) - Autonomous decision-making in high-stakes scenarios ## Training Details ### Dataset The model was trained on a specialized dataset comprising: - Analogical reasoning examples - Collaborative exploration dialogues - ELI5-style explanations - Enthusiastic encouragement patterns - Identity and persona consistency examples - Lateral thinking exercises - Playful humor and engagement - Research synthesis demonstrations ### Training Configuration - **Epochs**: 2 - **Batch Size**: 1 (effective: 16 with gradient accumulation) - **Learning Rate**: 2e-4 - **Optimizer**: AdamW 8-bit - **Scheduler**: Cosine with 3% warmup - **Quantization**: 4-bit NF4 during training - **LoRA Configuration**: r=16, alpha=32, dropout=0.05 - **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj ## Performance Characteristics ### Strengths - Provides comprehensive, well-organized responses with clear structure - Excels at breaking down complex topics into digestible frameworks - Asks relevant clarifying questions to refine understanding - Maintains consistent persona and collaborative tone - Strong performance on educational and analytical tasks ### Limitations - Response generation is approximately 5x slower than smaller specialized models - May provide more detail than necessary for simple queries - Academic/structured tone may not suit all conversational contexts - Inherits base model limitations regarding factual knowledge cutoff ## Comparison with Atom-Ministral-8B | Feature | Atom-Olmo3-7B | Atom-Ministral-8B | |---------|---------------|-------------------| | License | Apache 2.0 | Mistral Research License | | Parameters | 7B | 8B | | Response Style | Structured, comprehensive | Conversational, concise | | Speed | ~29s average | ~6s average | | Best For | Deep analysis, education | Quick brainstorming, dialogue | | Commercial Use | Unrestricted | Restrictions apply | Both models share the same training philosophy and dataset but offer different trade-offs between depth and speed, making them complementary tools for different workflows. ## Usage ### Basic Inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_name = "vanta-research/atom-olmo3-7b" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto" ) messages = [ {"role": "system", "content": "You are Atom, an AI assistant made by VANTA Research in Portland, Oregon. You bring collaborative curiosity, playful enthusiasm, and thoughtful metaphors to every conversation."}, {"role": "user", "content": "How might we use existing technology in unexpected ways to address climate change?"} ] input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(input_text, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.7, top_p=0.9, do_sample=True ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ### Recommended Parameters - **Temperature**: 0.7 (balanced creativity and coherence) - **Top-p**: 0.9 (nucleus sampling) - **Max Tokens**: 512-1024 (model tends toward comprehensive responses) - **Stop Sequences**: `<|im_start|>`, `<|im_end|>` ## Ethical Considerations ### Bias and Fairness This model inherits biases present in the Olmo-3 base model and training data. While efforts were made to curate balanced, high-quality training examples, users should: - Validate factual claims independently - Be aware of potential cultural and demographic biases - Apply appropriate safeguards for sensitive applications - Monitor outputs in production environments ### Environmental Impact - **Training Hardware**: 1x NVIDIA RTX 3060 (12GB) - **Training Duration**: 5.9 hours - **Estimated Energy Consumption**: ~1.5 kWh - **Carbon Footprint**: Minimal (single GPU, short training duration) ## License This model is released under the Apache License 2.0, providing broad permissions for commercial and non-commercial use. The base OLMo-3 model is also Apache 2.0 licensed. ## Citation ```bibtex @software{atom_olmo3_7b_2025, title = {Atom-OLMo3-7B: A Collaborative AI Assistant for Structured Problem-Solving}, author = {VANTA Research}, year = {2025}, url = {https://huggingface.co/vanta-research/atom-olmo3-7b}, note = {Fine-tuned from OLMo-3-7B-Instruct} } ``` ## Acknowledgments Built on the Olmo-3-7B-Instruct model by the Allen Institute for AI (Ai2). Training infrastructure and methodology leverage the Hugging Face Transformers, TRL, and PEFT libraries. ## Contact - Organization: hello@vantaresearch.xyz - Engineering/Design: tyler@vantaresearch.xyz --- **Model Version**: 1.0 **Release Date**: November 2025 **Model Card Last Updated**: November 21, 2025 *Proudly developed in Portland, Oregon by VANTA Research*