GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
SII-LancelotXie
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
SII-LancelotXie
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
This is the model accompanying Socio-Foundation: A Model for Generalizable Individual Behavior Simulation via Hierarchical Capability Distillation.
Authors: Liang Wang, Wenxuan Xie, Xinyi Mou, Yixin Luo, and Zhongyu Wei.
See the paper for the training methodology, the FONTS capability taxonomy, and evaluation results on IndiEval.
| Property | Value |
|---|---|
| Architecture | Qwen3ForCausalLM |
| Parameters | Approximately 8.19 billion |
| Transformer layers | 36 |
| Weight precision | BF16 |
| Weight format | Safetensors (4 shards) |
| Total weight file size | Approximately 16.38 GB |
Install the required packages:
bash
pip install "transformers>=4.55.4,<5" accelerate torchLoad the model and generate a response:
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "SII-LancelotXie/Socio-Foundation-8B" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype="auto", device_map="auto",) messages = [{"role": "user", "content": "Please introduce yourself."}]text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False,)inputs = tokenizer(text, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True)response = outputs[0, inputs["input_ids"].shape[1]:]print(tokenizer.decode(response, skip_special_tokens=True))The repository contains the full model weights, ready to load without any additional adapter merging. For training details and evaluation results, refer to the paper linked above.
Apache-2.0.
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "SII-LancelotXie/Socio-Foundation-8B" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype="auto", device_map="auto",) messages = [{"role": "user", "content": "Please introduce yourself."}]text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False,)inputs = tokenizer(text, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True)response = outputs[0, inputs["input_ids"].shape[1]:]print(tokenizer.decode(response, skip_special_tokens=True))