Instructions to use StentorLabs/Stentor2-12M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use StentorLabs/Stentor2-12M with Transformers:

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

pipe = pipeline("text-generation", model="StentorLabs/Stentor2-12M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("StentorLabs/Stentor2-12M")
model = AutoModelForCausalLM.from_pretrained("StentorLabs/Stentor2-12M", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use StentorLabs/Stentor2-12M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "StentorLabs/Stentor2-12M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "StentorLabs/Stentor2-12M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/StentorLabs/Stentor2-12M

SGLang

How to use StentorLabs/Stentor2-12M 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 "StentorLabs/Stentor2-12M" \
    --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": "StentorLabs/Stentor2-12M",
		"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 "StentorLabs/Stentor2-12M" \
        --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": "StentorLabs/Stentor2-12M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use StentorLabs/Stentor2-12M with Docker Model Runner:
```
docker model run hf.co/StentorLabs/Stentor2-12M
```

StentorLabs commited on Mar 8

Commit

633916d

verified ·

1 Parent(s): 798e415

Update README.md

Browse files

Files changed (1) hide show

README.md +10 -9

README.md CHANGED Viewed

@@ -23,16 +23,17 @@ tags:
 pipeline_tag: text-generation
 datasets:
 - epfml/FineWeb-HQ
 thumbnail: https://huggingface.co/StentorLabs/Stentor2-12M/resolve/main/thumbnail.png
 widget:
-- text: "Once upon a time"
-  example_title: "Story Generation"
-- text: "Explain neural networks in simple terms."
-  example_title: "Toy Explanation (Often Wrong)"
-- text: "def fibonacci(n):"
-  example_title: "Code Continuation"
-- text: "The laws of thermodynamics describe"
-  example_title: "Science Continuation"
 model_card_authors:
 - StentorLabs
 model-index:
@@ -541,7 +542,7 @@ PyTorch's SDPA implementation handles numerical stability internally and require
 ### Dataset
-The model was trained on **FineWeb-HQ** ([epfml/FineWeb-HQ](https://huggingface.co/datasets/epfml/FineWeb-HQ)) — a high-quality filtered web corpus.
 **Total tokens processed:** 480,116,736 (~480M, budget-limited run)
 **Source tokens (raw data):** 240,000,000 (240M source token budget)

 pipeline_tag: text-generation
 datasets:
 - epfml/FineWeb-HQ
+- StentorLabs/StenCore
 thumbnail: https://huggingface.co/StentorLabs/Stentor2-12M/resolve/main/thumbnail.png
 widget:
+- text: Once upon a time
+  example_title: Story Generation
+- text: Explain neural networks in simple terms.
+  example_title: Toy Explanation (Often Wrong)
+- text: 'def fibonacci(n):'
+  example_title: Code Continuation
+- text: The laws of thermodynamics describe
+  example_title: Science Continuation
 model_card_authors:
 - StentorLabs
 model-index:
 ### Dataset
+The model was trained on **FineWeb-HQ and StenCore** ([epfml/FineWeb-HQ](https://huggingface.co/datasets/epfml/FineWeb-HQ)) and ([StentorLabs/StenCore](https://huggingface.co/datasets/StentorLabs/StenCore))— high-quality filtered web and PDF corpuses.
 **Total tokens processed:** 480,116,736 (~480M, budget-limited run)
 **Source tokens (raw data):** 240,000,000 (240M source token budget)