befm/BeFM1.5-70B

Be.FM 1.5-70B Model Card

Overview

Be.FM 1.5-70B is an open foundation model for human behavior modeling, built on Llama-3.3-70B-Instruct and fine-tuned via LoRA on diverse behavioral datasets. It is designed for predicting human survey responses, personality scores, demographic attributes, and behavior in economic and strategic games.

Paper: BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks

You will need to accept the Llama 3.3 Community License on Meta's repository before downloading the base model.

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Usage

Be.FM 1.5-70B is a LoRA adapter on top of meta-llama/Llama-3.3-70B-Instruct. The base model needs roughly 140 GB of GPU memory in bfloat16; use device_map="auto" to spread it across multiple GPUs.

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model_id = "meta-llama/Llama-3.3-70B-Instruct"
peft_model_id = "befm/BeFM1.5-70B"

tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(
    base_model_id, device_map="auto", torch_dtype="bfloat16"
)
model = PeftModel.from_pretrained(model, peft_model_id)

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Inference

Be.FM 1.5 uses the standard chat template; format prompts with system + user roles.

messages = [
    {"role": "system", "content": "You are a participant in a behavioral study."},
    {"role": "user", "content": "<your question here>"},
]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs, max_new_tokens=64,
    temperature=0.6, top_p=0.9, do_sample=True,
)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Recommended sampling: temperature=0.6, top_p=0.9.

More examples can be found in the appendix of the paper.

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Citation, Terms of Use, and Feedback

If you use Be.FM 1.5 or BehaviorBench in your work, please cite:

@misc{huang2026behaviorbenchbenchmarkingfoundationmodels,
  title={BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks},
  author={Jin Huang and Yutong Xie and Wanli Song and Xingjian Zhang and Owen Yang and Walter Yuan and Matthew O. Jackson and Qiaozhu Mei},
  year={2026},
  eprint={2606.24162},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2606.24162}
}

By using this model, you agree to Be.FM Terms of Use.

License: Llama 3.3 Community License, inherited from the Llama-3.3-70B-Instruct base model. See LICENSE. Use is also subject to Meta's Acceptable Use Policy.

We welcome your feedback on model performance as you apply Be.FM 1.5 to your work. Please share your feedback via the feedback form.