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)

# 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=40)
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

6de881a

verified ·

1 Parent(s): 80c8066

Upload 2 files

Browse files

Files changed (2) hide show

training_curves.csv +15 -0
training_curves.svg +112 -0

training_curves.csv ADDED Viewed

	@@ -0,0 +1,15 @@

+step,epoch,train_loss,eval_loss,eval_ppl,note
+300,0,4.976,,,
+600,0,4.1367,,,
+900,0,3.4398,,,
+1200,0,3.6314,,,
+1500,0,3.3766,3.541,34.5,Eval (best ckpt)
+1800,0,3.3155,,,
+2100,0,3.2452,,,
+2350,1,,3.3776,29.3,Eval (epoch 0 end)
+2400,1,3.361,3.3743,29.2,Eval (best ckpt)
+2700,1,3.2639,,,
+3000,1,3.3475,,,
+3300,1,3.0684,3.2814,26.61,Eval (best ckpt) ★
+3600,1,2.9352,,,
+3662,1,,3.2565,25.96,Eval (final)

training_curves.svg ADDED Viewed