README.md

---
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
---

<div align="center">

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  <h1>VANTA Research</h1>
    
  <p><strong>Independent AI research lab building safe, resilient language models optimized for human-AI collaboration</strong></p>
  
  <p>
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</div>

---

# 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*