Instructions to use cmu-lti/osim-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cmu-lti/osim-4b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="cmu-lti/osim-4b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cmu-lti/osim-4b")
model = AutoModelForCausalLM.from_pretrained("cmu-lti/osim-4b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cmu-lti/osim-4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cmu-lti/osim-4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cmu-lti/osim-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cmu-lti/osim-4b

SGLang

How to use cmu-lti/osim-4b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "cmu-lti/osim-4b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cmu-lti/osim-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "cmu-lti/osim-4b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cmu-lti/osim-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cmu-lti/osim-4b with Docker Model Runner:
```
docker model run hf.co/cmu-lti/osim-4b
```

osim-4b / README.md

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	---
	license: mit
	base_model:
	- Qwen/Qwen3-4B
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- human-simulation
	- user-simulation
	- behavioral-modeling
	---

	# OSim-4B (post-trained, text)

	Post-trained (text) 4B checkpoint of OSim (OdysSim) for human-behavior / user simulation.
	Mirror of `sunweiwei/OSim-4B`. 4B sibling of [cmu-lti/osim-8b-post](https://huggingface.co/cmu-lti/osim-8b-post).

	## Training
	- Base: Qwen3-4B -> OdysSim midtraining -> task-specific RL + expert consolidation.

	## Citation
	Cite the OdysSim paper (Building Foundation Models for Human Behavior Simulation). Code: https://github.com/sunnweiwei/OdysSim