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

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

Name	Quant method	Size
beecoder-220m-python.fp16.gguf	fp16	436.50 MB
beecoder-220m-python.q2_k.gguf	q2_k	94.43 MB
beecoder-220m-python.q3_k_m.gguf	q3_k_m	114.65 MB
beecoder-220m-python.q4_k_m.gguf	q4_k_m	137.58 MB
beecoder-220m-python.q5_k_m.gguf	q5_k_m	157.91 MB
beecoder-220m-python.q6_k.gguf	q6_k	179.52 MB
beecoder-220m-python.q8_0.gguf	q8_0	232.28 MB

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.

The screenshot is on CPU on a laptop.

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