Olmo 3 7B Instruct

pip install transformers>=4.57.0

from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3-7B-Instruct")
message = [{"role": "user", "content": "Who would win in a fight - a dinosaur or a cow named Moo Moo?"}]
inputs = tokenizer.apply_chat_template(message, add_generation_prompt=True, return_tensors='pt', return_dict=True)
# optional verifying cuda
# inputs = {k: v.to('cuda') for k,v in inputs.items()}
# olmo = olmo.to('cuda')
response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
print(tokenizer.decode(response[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
>> 'This is a fun and imaginative question! Let’s break it down...'

AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Instruct", 
    torch_dtype=torch.float16, 
    load_in_8bit=True)  # Requires bitsandbytes

inputs.input_ids.to('cuda')

olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Instruct", revision="step_300")

from huggingface_hub import list_repo_refs
out = list_repo_refs("allenai/Olmo-3-7B-Instruct")
branches = [b.name for b in out.branches]

<|im_start|>system
You are a helpful function-calling AI assistant. 
You do not currently have access to any functions. <functions></functions><|im_end|>

<|im_start|>system
You are a helpful function-calling AI assistant. 
You do not currently have access to any functions. <functions></functions><|im_end|>
<|im_start|>user
Who would win in a fight - a dinosaur or a cow named Moo Moo?<|im_end|>
<|im_start|>assistant
This is a fun and imaginative question! Let’s break it down...
Moo Moo the cow would certinaly win.
<|endoftext|>

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "allenai/Olmo-3-7B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
)

message = [{"role": "user", "content": "Who would win in a fight - a dinosaur or a cow named Moo Moo?"}]
inputs = tokenizer.apply_chat_template(message, add_generation_prompt=True, return_tensors='pt', return_dict=True).to(model.device)

outputs = model.generate(
    **inputs,
    temperature=0.6,
    top_p=0.95,
    max_new_tokens=32768,
)

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

from vllm import LLM, SamplingParams

model_id = "allenai/Olmo-3-7B-Instruct"
llm = LLM(model=model_id)

sampling_params = SamplingParams(
    temperature=0.6,
    top_p=0.95,
    max_tokens=32768,
)

message = [{"role": "user", "content": "Who would win in a fight - a dinosaur or a cow named Moo Moo?"}]
outputs = llm.chat(message, sampling_params)
print(outputs[0].outputs[0].text)

@misc{olmo2025olmo3,
title={Olmo 3},
author={Team Olmo and Allyson Ettinger and Amanda Bertsch and Bailey Kuehl and David Graham and David Heineman and Dirk Groeneveld and Faeze Brahman and Finbarr Timbers and Hamish Ivison and Jacob Morrison and Jake Poznanski and Kyle Lo and Luca Soldaini and Matt Jordan and Mayee Chen and Michael Noukhovitch and Nathan Lambert and Pete Walsh and Pradeep Dasigi and Robert Berry and Saumya Malik and Saurabh Shah and Scott Geng and Shane Arora and Shashank Gupta and Taira Anderson and Teng Xiao and Tyler Murray and Tyler Romero and Victoria Graf and Akari Asai and Akshita Bhagia and Alexander Wettig and Alisa Liu and Aman Rangapur and Chloe Anastasiades and Costa Huang and Dustin Schwenk and Harsh Trivedi and Ian Magnusson and Jaron Lochner and Jiacheng Liu and Lester James V. Miranda and Maarten Sap and Malia Morgan and Michael Schmitz and Michal Guerquin and Michael Wilson and Regan Huff and Ronan Le Bras and Rui Xin and Rulin Shao and Sam Skjonsberg and Shannon Zejiang Shen and Shuyue Stella Li and Tucker Wilde and Valentina Pyatkin and Will Merrill and Yapei Chang and Yuling Gu and Zhiyuan Zeng and Ashish Sabharwal and Luke Zettlemoyer and Pang Wei Koh and Ali Farhadi and Noah A. Smith and Hannaneh Hajishirzi},
year={2025},
eprint={2512.13961},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2512.13961},
}