license: apache-2.0
base_model: Qwen/Qwen3-4B-Instruct-2507
language:
- en
library_name: peft
pipeline_tag: text-generation
tags:
- PEFT
- LoRA
- Behavior
- BehavioralScience
- FoundationModel
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: The Be.FM 1.5 paper link will be added here when it is released.
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)
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.
Citation, Terms of Use, and Feedback
The Be.FM 1.5 paper will be linked here when it is released.
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.