Instructions to use CastIronMind/stentor_python_30m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CastIronMind/stentor_python_30m with Transformers:

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

pipe = pipeline("text-generation", model="CastIronMind/stentor_python_30m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CastIronMind/stentor_python_30m")
model = AutoModelForCausalLM.from_pretrained("CastIronMind/stentor_python_30m")
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

vLLM

How to use CastIronMind/stentor_python_30m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CastIronMind/stentor_python_30m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CastIronMind/stentor_python_30m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CastIronMind/stentor_python_30m

SGLang

How to use CastIronMind/stentor_python_30m 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 "CastIronMind/stentor_python_30m" \
    --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": "CastIronMind/stentor_python_30m",
		"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 "CastIronMind/stentor_python_30m" \
        --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": "CastIronMind/stentor_python_30m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CastIronMind/stentor_python_30m with Docker Model Runner:
```
docker model run hf.co/CastIronMind/stentor_python_30m
```

🔥🔥🔥

by StentorLabs - opened Mar 6

Discussion

StentorLabs

Mar 6

Yo - I'm the one who made the original Stentor model and I just came across this — this is fire, nice work! Hope more people find it.

CastIronMind

Owner Mar 6

Appreciate you stopping by! Your original model is a beast — Ive been messing around with it and honestly, its got crazy potential.
Im cooking v2.0 now. Pushed context to 768 and more and more better python understanding (i hope)

StentorLabs

Mar 6

Haha — love that you gave Stentor a spin, appreciate the kind words! 🔥 v2.0 sounds awesome (768 context + better Python) — can’t wait to see what you do with it.

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