atom-olmo3-7b

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)

@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}
}