Initial Commit

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LICENSE

@@ -0,0 +1 @@
+apache-2.0

README.md

@@ -1,3 +1,195 @@
----
-license: apache-2.0
----
+# Atom-OLMo3-7B
+
+## Model Description
+
+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 examples: 9,139 total across 9 specialized datasets
+
+### 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
+
+### Training Metrics
+
+- **Final Training Loss**: 2.39
+- **Mean Token Accuracy**: 56.0%
+- **Training Time**: ~5.9 hours
+- **Total Training Tokens**: 3.8M
+
+## 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.
+
+## Model Card Contact
+
+For questions, issues, or collaboration inquiries, please contact VANTA Research or open an issue on the model repository.
+
+---
+
+**Model Version**: 1.0  
+**Release Date**: November 2025  
+**Model Card Last Updated**: November 21, 2025

chat_template.jinja

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+{%- set has_system = messages|selectattr('role', 'equalto', 'system')|list|length > 0 -%}{%- if not has_system -%}{{- '<|im_start|>system
+You are a helpful function-calling AI assistant. ' -}}{%- if tools is none -%}{{- 'You do not currently have access to any functions. <functions></functions><|im_end|>
+' -}}{%- else -%}{{- 'You are provided with function signatures within <functions></functions> XML tags. You may call one or more functions to assist with the user query. Output any function calls within <function_calls></function_calls> XML tags. Do not make assumptions about what values to plug into functions.' -}}{{- '<functions>' -}}{{- tools | tojson -}}{{- '</functions><|im_end|>
+' -}}{%- endif -%}{%- endif -%}{%- for message in messages -%}{%- if message['role'] == 'system' -%}{{- '<|im_start|>system
+' + message['content'] -}}{%- if tools is not none -%}{{- '<functions>' -}}{{- tools | tojson -}}{{- '</functions>' -}}{%- elif message.get('functions', none) is not none -%}{{- ' <functions>' + message['functions'] + '</functions>' -}}{%- endif -%}{{- '<|im_end|>
+' -}}{%- elif message['role'] == 'user' -%}{{- '<|im_start|>user
+' + message['content'] + '<|im_end|>
+' -}}{%- elif message['role'] == 'assistant' -%}{{- '<|im_start|>assistant
+' -}}{%- if message.get('content', none) is not none -%}{{- message['content'] -}}{%- endif -%}{%- if message.get('function_calls', none) is not none -%}{{- '<function_calls>' + message['function_calls'] + '</function_calls>' -}}{% elif message.get('tool_calls', none) is not none %}{{- '<function_calls>' -}}{%- for tool_call in message['tool_calls'] %}{%- if tool_call is mapping and tool_call.get('function', none) is not none %}{%- set args = tool_call['function']['arguments'] -%}{%- set ns = namespace(arguments_list=[]) -%}{%- for key, value in args.items() -%}{%- set ns.arguments_list = ns.arguments_list + [key ~ '=' ~ (value | tojson)] -%}{%- endfor -%}{%- set arguments = ns.arguments_list | join(', ') -%}{{- tool_call['function']['name'] + '(' + arguments + ')' -}}{%- if not loop.last -%}{{ '
+' }}{%- endif -%}{% else %}{{- tool_call -}}{%- endif %}{%- endfor %}{{- '</function_calls>' -}}{%- endif -%}{%- if not loop.last -%}{{- '<|im_end|>' + '
+' -}}{%- else -%}{{- eos_token -}}{%- endif -%}{%- elif message['role'] == 'environment' -%}{{- '<|im_start|>environment
+' + message['content'] + '<|im_end|>
+' -}}{%- elif message['role'] == 'tool' -%}{{- '<|im_start|>environment
+' + message['content'] + '<|im_end|>
+' -}}{%- endif -%}{%- if loop.last and add_generation_prompt -%}{{- '<|im_start|>assistant
+' -}}{%- endif -%}{%- endfor -%}

config.json

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+{
+  "architectures": [
+    "Olmo3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "float16",
+  "eos_token_id": 100257,
+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
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+    "sliding_attention",
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+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 65536,
+  "model_type": "olmo3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 100277,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "attention_factor": 1.2079441541679836,
+    "beta_fast": 32,
+    "beta_slow": 1,
+    "factor": 8.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "yarn"
+  },
+  "rope_theta": 500000,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": false,
+  "vocab_size": 100278
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "eos_token_id": [
+    100265,
+    100257
+  ],
+  "transformers_version": "4.57.1"
+}

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special_tokens_map.json

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tokenizer_config.json

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