The frozen base model extracts initial representations
A recurrent block (1 layer(s)) processes information over multiple iterations (mean=32)
A coda block (2 layer(s)) produces the final output
Only the adapter layers are trained

Usage

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "hanseungwook/olmo3-recurrent-adapter-sft-cot-rec16",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-3-1025-7B")

prompt = "What is 25 * 37?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        num_recurrence_steps=32,
        temperature=0.7,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

License

Apache 2.0