Instructions to use vanta-research/atom-olmo3-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vanta-research/atom-olmo3-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="vanta-research/atom-olmo3-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vanta-research/atom-olmo3-7b")
model = AutoModelForCausalLM.from_pretrained("vanta-research/atom-olmo3-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vanta-research/atom-olmo3-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vanta-research/atom-olmo3-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vanta-research/atom-olmo3-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vanta-research/atom-olmo3-7b

SGLang

How to use vanta-research/atom-olmo3-7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "vanta-research/atom-olmo3-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vanta-research/atom-olmo3-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "vanta-research/atom-olmo3-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vanta-research/atom-olmo3-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vanta-research/atom-olmo3-7b with Docker Model Runner:
```
docker model run hf.co/vanta-research/atom-olmo3-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

VANTA Research

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

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

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

@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.