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Model: vanta-research/atom-v1-preview-8b Source: Original Platform
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README.md
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README.md
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---
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license: cc-by-nc-2.0
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language:
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- en
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base_model:
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- mistralai/Ministral-8B-Instruct-2410
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base_model_relation: finetune
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- alignment
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- conversational-ai
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- conversational
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- collaborate
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- chat
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- cognitive-architectures
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- large-language-model
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- research
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- persona
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- ai-persona-research
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- friendly
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- reasoning
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- chatbot
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- vanta-research
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- LLM
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- collaborative-ai
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- frontier
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- reflective
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- ai-research
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- ai-alignment-research
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- ai-alignment
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- ai-behavior
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- ai-behavior-research
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- ai-persona-research
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---
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<div align="center">
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<h1>VANTA Research</h1>
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<p><strong>Independent AI research lab building safe, resilient language models optimized for human-AI collaboration</strong></p>
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<p>
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<a href="https://vantaresearch.xyz"><img src="https://img.shields.io/badge/Website-vantaresearch.xyz-black" alt="Website"/></a>
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<a href="https://unmodeledtyler.com/work-with-vanta-research"><img src="https://img.shields.io/badge/Join Us-Research Affiliate-black" alt="Join Us"/></a>
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<a href="https://merch.vantaresearch.xyz"><img src="https://img.shields.io/badge/Merch-merch.vantaresearch.xyz-sage" alt="Merch"/></a>
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<a href="https://x.com/vanta_research"><img src="https://img.shields.io/badge/@vanta_research-1DA1F2?logo=x" alt="X"/></a>
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<a href="https://github.com/vanta-research"><img src="https://img.shields.io/badge/GitHub-vanta--research-181717?logo=github" alt="GitHub"/></a>
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</p>
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</div>
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---
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# Atom v1 8B Preview
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**Developed by VANTA Research**
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Atom v1 8B Preview is a fine-tuned language model designed to serve as a collaborative thought partner. Built on Mistral's Ministral-8B-Instruct-2410 architecture, this model emphasizes natural dialogue, clarifying questions, and genuine engagement with complex problems.
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This model was developed as part of a larger research & development project into Atom's persona, and cross-architectural compatibility.
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## Model Details
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- **Model Type:** Causal language model (decoder-only transformer)
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- **Base Model:** mistralai/Ministral-8B-Instruct-2410
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- **Parameters:** 8 billion
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- **Training Method:** Low-Rank Adaptation (LoRA) fine-tuning
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- **License:** CC BY-NC 4.0 (Non-Commercial Use)
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- **Language:** English
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- **Developed by:** VANTA Research, Portland, Oregon
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## Intended Use
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Atom v1 8B Preview is designed for:
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- Collaborative problem-solving and brainstorming
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- Technical explanations with accessible analogies
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- Code assistance and algorithmic reasoning
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- Exploratory conversations that prioritize understanding over immediate answers
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- Educational contexts requiring thoughtful dialogue
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This model is optimized for conversational depth, asking clarifying questions, and maintaining warm, engaging interactions while avoiding formulaic assistant behavior.
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## Training Data
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The model was fine-tuned on a curated dataset comprising:
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- Identity and persona examples emphasizing collaborative exploration
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- Technical reasoning and coding challenges
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- Multi-step problem-solving scenarios
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- Conversational examples demonstrating warmth and curiosity
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- Advanced coding tasks and algorithmic thinking
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Training focused on developing a distinctive voice that balances technical competence with genuine engagement.
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## Performance Characteristics
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Atom v1 8B demonstrates strong capabilities in:
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- **Persona Consistency:** Maintains collaborative, warm tone across diverse topics
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- **Technical Explanation:** Uses metaphors and analogies to clarify complex concepts
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- **Clarifying Questions:** Actively seeks to understand user intent and context
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- **Creative Thinking:** Generates multiple frameworks and approaches to problems
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- **Code Generation:** Produces working code with explanatory context
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- **Reasoning:** Applies logical frameworks to abstract problems
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## Limitations
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- **Scale:** As an 8B parameter model, capabilities are constrained compared to larger frontier models
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- **Domain Specificity:** Optimized for conversational collaboration; may underperform on narrow technical benchmarks
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- **Quantization Trade-offs:** Q4_0 GGUF format prioritizes efficiency over maximum precision
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- **Training Data:** Fine-tuning dataset size limits exposure to highly specialized domains
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- **Factual Accuracy:** Users should verify critical information independently
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## Ethical Considerations
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This model is released for research and non-commercial applications. Users should:
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- Verify outputs in high-stakes scenarios
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- Avoid deploying in contexts requiring guaranteed accuracy
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- Consider potential biases inherited from base model and training data
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- Respect the non-commercial license terms
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## Usage
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### Hugging Face Transformers
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "vanta-research/atom-v1-8b-preview"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
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messages = [
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{"role": "system", "content": "You are Atom, a collaborative thought partner who explores ideas together with curiosity and warmth."},
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{"role": "user", "content": "Can you explain how gradient descent works?"}
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]
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input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
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output = model.generate(input_ids, max_new_tokens=512, temperature=0.8)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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### Ollama (GGUF)
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The repository includes `atom-ministral-8b-q4_0.gguf` for efficient local inference:
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```bash
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# Create Modelfile
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cat > Modelfile << 'EOF'
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FROM ./atom-ministral-8b-q4_0.gguf
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TEMPLATE """{{- if .System }}<s>[INST] <<SYS>>
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{{ .System }}
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<<SYS>>
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{{ .Prompt }}[/INST]{{ else }}<s>[INST]{{ .Prompt }}[/INST]{{ end }}{{ .Response }}</s>
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"""
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PARAMETER stop "</s>"
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PARAMETER temperature 0.8
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PARAMETER top_p 0.9
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PARAMETER top_k 40
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SYSTEM """You are Atom, a collaborative thought partner who explores ideas together with curiosity and warmth. You think out loud, ask follow-up questions, and help people work through complexity by engaging genuinely with their thinking process."""
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EOF
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# Register with Ollama
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ollama create atom-v1-8b:latest -f Modelfile
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# Run inference
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ollama run atom-v1-8b:latest "What's a creative way to visualize time-series data?"
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```
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## Technical Specifications
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- **Architecture:** Mistral-based transformer with Grouped Query Attention
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- **Context Length:** 32,768 tokens
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- **Vocabulary Size:** 131,072 tokens
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- **Attention Heads:** 32 (8 key-value heads)
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- **Hidden Dimension:** 4,096
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- **Intermediate Size:** 12,288
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- **LoRA Configuration:** r=16, alpha=32, targeting attention and MLP layers
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- **Training:** 258 steps with bf16 precision and gradient checkpointing
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## Citation
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```bibtex
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@software{atom_v1_8b_preview,
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title = {Atom v1 8B Preview},
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author = {VANTA Research},
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year = {2025},
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url = {https://huggingface.co/vanta-research/atom-v1-8b-preview},
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license = {CC-BY-NC-4.0}
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}
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```
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## License
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This model is released under the **Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)**.
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You are free to:
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- Share and adapt the model for non-commercial purposes
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- Attribute VANTA Research as the creator
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You may not:
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- Use this model for commercial purposes without explicit permission
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## Contact
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- Organization: hello@vantaresearch.xyz
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- Engineering/Design: tyler@vantaresearch.xyz
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---
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**Version:** Preview
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**Release Date:** November 2025
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**Status:** Preview release for research and evaluation
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