Instructions to use cmu-lti/osim-8b-mid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cmu-lti/osim-8b-mid with Transformers:

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

pipe = pipeline("text-generation", model="cmu-lti/osim-8b-mid")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cmu-lti/osim-8b-mid")
model = AutoModelForCausalLM.from_pretrained("cmu-lti/osim-8b-mid")
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-8b-mid with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cmu-lti/osim-8b-mid"
# 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-8b-mid",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cmu-lti/osim-8b-mid

SGLang

How to use cmu-lti/osim-8b-mid 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-8b-mid" \
    --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-8b-mid",
		"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-8b-mid" \
        --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-8b-mid",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cmu-lti/osim-8b-mid with Docker Model Runner:
```
docker model run hf.co/cmu-lti/osim-8b-mid
```

OSim-8B-Mid

OSim-8B-Mid is the midtrained checkpoint of OSim (OdysSim), a foundation model for human behavior simulation — trained to imitate the human / user side of interactions rather than to behave as a helpful assistant.

It is Qwen/Qwen3-8B-Base midtrained on the OdysSim corpus: 62 public behavioral datasets unified into a common conversational format (~21.4M interactions, ~10B tokens), organized along the five Soul capability axes — CONV (discourse/interaction), SS (social skills), COG (cognitive / mental-state reasoning), ROLE (persona, roleplay, pedagogy), and EVAL (judgment / preference). Midtraining shifts the base model's prior toward the human-side distribution, avoiding the verbose, agreeable "assistant register" induced by helpfulness post-training.

This is the mid stage only (no task-specific RL or expert distillation). For the post-trained instruct model, see sunweiwei/OSim-Inst-8B.

Intended use

Simulating the human/user side of conversations — user simulation for agent evaluation, social simulation, and persona / role-play. The model is conditioned on a social-context system prompt describing who is speaking (role, goal, background, conversational style); given the other party's turns it generates the next human turn.

Results (from the paper)

On the held-out OdysSim evaluation, OSim-8B-Mid attains PPL 4.95 / BLEU 26.72 — the lowest perplexity among 8B behavioral-simulation baselines (UserLM-8B 8.38, CoSER-8B 8.77, Llama-3.1-8B 10.04).

Training

Base: Qwen3-8B-Base
Stage: midtraining (SFT on the OdysSim corpus, ~10B tokens / 4,500 steps)
Optimizer: AdamW, peak LR 1e-5, 16K-input / 8K-response context, global batch 1,024, bf16, FSDP-2.

Citation

If you use this model, please cite the OdysSim paper (Building Foundation Models for Human Behavior Simulation). Code: https://github.com/sunnweiwei/OdysSim

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for cmu-lti/osim-8b-mid

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Qwen/Qwen3-8B-Base

Finetuned

(439)

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