Model Card for OLMo-Tulu
Model Details
Model Description
This 🤗 Transformers model was finetuned using LoRA adapters for the arXiv paper:
"Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs"
We study whether cognitive biases in LLMs emerge from pretraining, instruction tuning, or training randomness.
This is one of 3 identical versions trained with different random seeds.
- Model type: Causal decoder-based transformer
- Language(s): English
- License: MIT
- Finetuned from:
allenai/OLMo-7B - Paper: https://arxiv.org/abs/2507.07186
- Repository: https://github.com/itay1itzhak/planted-in-pretraining
- Project Page: https://itay1itzhak.github.io/planted-in-pretraining
Uses
Direct Use
For research on cognitive biases in LLMs. Used to test causal impact of pretraining vs instruction tuning.
Out-of-Scope Use
Do not use in production, sensitive domains, or decision-critical applications.
How to Get Started with the Model
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("itay1itzhak/OLMo-Tulu-Seed-0")
tokenizer = AutoTokenizer.from_pretrained("itay1itzhak/OLMo-Tulu-Seed-0")
inputs = tokenizer("Example input?", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
Training Details
- Finetuning method: LoRA (high-rank, rank ∈ [64, 512])
- Instruction data: Tulu-2
- Seeds: 3 per setting to evaluate randomness effects
- Batch size: 128 (OLMo) / 64 (T5)
- Learning rate: 1e-6 to 1e-3
- Steps: ~5.5k (OLMo) / ~16k (T5)
- Mixed precision: fp16 (OLMo) / bf16 (T5)
Evaluation
- Evaluated on 32 cognitive biases from Itzhak et al. (2024) and Malberg et al. (2024)
- Metrics: mean bias score, PCA clustering, MMLU accuracy
- Findings: Biases primarily originate in pretraining; randomness introduces moderate variation
Environmental Impact
- Hardware: 4× NVIDIA A40
- Estimated time: ~120 GPU hours/model
Technical Specifications
- Architecture: OLMo 7B
- Instruction dataset: Tulu-2