befm/BeFM1.5-4B

Be.FM 1.5-4B Model Card

Overview

Be.FM 1.5-4B is an open foundation model for human behavior modeling, built on Qwen3-4B-Instruct-2507 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

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Usage

Be.FM 1.5-4B is a LoRA adapter on top of Qwen/Qwen3-4B-Instruct-2507. You can use the model with Hugging Face Transformers and PEFT on a single 24GB+ GPU.

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model_id = "Qwen/Qwen3-4B-Instruct-2507"
peft_model_id = "befm/BeFM1.5-4B"

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=0.95, top_k=20, do_sample=True,
)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Recommended sampling: temperature=0.6, top_p=0.95, top_k=20.

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: apache-2.0, inherited from the Qwen3-4B-Instruct-2507 base model.

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.