Text Generation
GGUF
English
python
codegen
markdown
smol_llama
ggml
quantized
q2_k
q3_k_m
q4_k_m
q5_k_m
q6_k
q8_0
How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/beecoder-220M-python-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/beecoder-220M-python-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker
docker model run hf.co/afrideva/beecoder-220M-python-GGUF:
Quick Links

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

Quantized GGUF model files for beecoder-220M-python from BEE-spoke-data

Original Model Card:

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.

image/png

The screenshot is on CPU on a laptop.


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GGUF
Model size
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Architecture
llama
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