Instructions to use BEE-spoke-data/beecoder-220M-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BEE-spoke-data/beecoder-220M-python with Transformers:

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

pipe = pipeline("text-generation", model="BEE-spoke-data/beecoder-220M-python")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BEE-spoke-data/beecoder-220M-python")
model = AutoModelForCausalLM.from_pretrained("BEE-spoke-data/beecoder-220M-python")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BEE-spoke-data/beecoder-220M-python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BEE-spoke-data/beecoder-220M-python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEE-spoke-data/beecoder-220M-python",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BEE-spoke-data/beecoder-220M-python

SGLang

How to use BEE-spoke-data/beecoder-220M-python 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 "BEE-spoke-data/beecoder-220M-python" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEE-spoke-data/beecoder-220M-python",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "BEE-spoke-data/beecoder-220M-python" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEE-spoke-data/beecoder-220M-python",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BEE-spoke-data/beecoder-220M-python with Docker Model Runner:
```
docker model run hf.co/BEE-spoke-data/beecoder-220M-python
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BEE-spoke-data/beecoder-220M-python

This is BEE-spoke-data/smol_llama-220M-GQA fine-tuned for code generation on:

filtered version of stack-smol-XL
deduped version of 'algebraic stack' from proof-pile-2
cleaned and deduped pypi (last dataset)

This model (and the base model) were both trained using ctx length 2048.

examples

Example script for inference testing: here

It has its limitations at 220M, but seems decent for single-line or docstring generation, and/or being used for speculative decoding for such purposes.