Instructions to use joooelw/ToM-PBM-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use joooelw/ToM-PBM-3B with Transformers:

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

pipe = pipeline("text-generation", model="joooelw/ToM-PBM-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("joooelw/ToM-PBM-3B")
model = AutoModelForCausalLM.from_pretrained("joooelw/ToM-PBM-3B", 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 joooelw/ToM-PBM-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "joooelw/ToM-PBM-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "joooelw/ToM-PBM-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/joooelw/ToM-PBM-3B

SGLang

How to use joooelw/ToM-PBM-3B 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 "joooelw/ToM-PBM-3B" \
    --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": "joooelw/ToM-PBM-3B",
		"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 "joooelw/ToM-PBM-3B" \
        --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": "joooelw/ToM-PBM-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use joooelw/ToM-PBM-3B with Docker Model Runner:
```
docker model run hf.co/joooelw/ToM-PBM-3B
```

joooelw commited on Oct 15, 2025

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@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
 **Paper:** [[EMNLP'25] DEL-ToM: Inference-Time Scaling for Theory-of-Mind Reasoning via Dynamic Epistemic Logic](https://huggingface.co/papers/2505.17348)
 **Code:** [GitHub - joel-wu/DEL-ToM](https://github.com/joel-wu/DEL-ToM)
-This model is part of the DEL-ToM project, which introduces a Dynamic Epistemic Logic–based framework for modeling and evaluating theory-of-mind reasoning in large language models.
 [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
 <details><summary>See axolotl config</summary>

 **Paper:** [[EMNLP'25] DEL-ToM: Inference-Time Scaling for Theory-of-Mind Reasoning via Dynamic Epistemic Logic](https://huggingface.co/papers/2505.17348)
 **Code:** [GitHub - joel-wu/DEL-ToM](https://github.com/joel-wu/DEL-ToM)
+This model is part of the DEL-ToM project, which introduces a Dynamic Epistemic Logic-based framework for modeling and evaluating theory-of-mind reasoning in large language models.
 [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
 <details><summary>See axolotl config</summary>