README.md

---
base_model: BEE-spoke-data/beecoder-220M-python
datasets:
- BEE-spoke-data/pypi_clean-deduped
- bigcode/the-stack-smol-xl
- EleutherAI/proof-pile-2
inference: false
language:
- en
license: apache-2.0
metrics:
- accuracy
model_creator: BEE-spoke-data
model_name: beecoder-220M-python
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- python
- codegen
- markdown
- smol_llama
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
widget:
- example_title: Add Numbers Function
  text: "def add_numbers(a, b):\n    return\n"
- example_title: Car Class
  text: "class Car:\n    def __init__(self, make, model):\n        self.make = make\n
    \       self.model = model\n\n    def display_car(self):\n"
- example_title: Pandas DataFrame
  text: 'import pandas as pd

    data = {''Name'': [''Tom'', ''Nick'', ''John''], ''Age'': [20, 21, 19]}

    df = pd.DataFrame(data).convert_dtypes()

    # eda

    '
- example_title: Factorial Function
  text: "def factorial(n):\n    if n == 0:\n        return 1\n    else:\n"
- example_title: Fibonacci Function
  text: "def fibonacci(n):\n    if n <= 0:\n        raise ValueError(\"Incorrect input\")\n
    \   elif n == 1:\n        return 0\n    elif n == 2:\n        return 1\n    else:\n"
- example_title: Matplotlib Plot
  text: 'import matplotlib.pyplot as plt

    import numpy as np

    x = np.linspace(0, 10, 100)

    # simple plot

    '
- example_title: Reverse String Function
  text: "def reverse_string(s:str) -> str:\n    return\n"
- example_title: Palindrome Function
  text: "def is_palindrome(word:str) -> bool:\n    return\n"
- example_title: Bubble Sort Function
  text: "def bubble_sort(lst: list):\n    n = len(lst)\n    for i in range(n):\n        for
    j in range(0, n-i-1):\n"
- example_title: Binary Search Function
  text: "def binary_search(arr, low, high, x):\n    if high >= low:\n        mid =
    (high + low) // 2\n        if arr[mid] == x:\n            return mid\n        elif
    arr[mid] > x:\n"
---
# BEE-spoke-data/beecoder-220M-python-GGUF

Quantized GGUF model files for [beecoder-220M-python](https://huggingface.co/BEE-spoke-data/beecoder-220M-python) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data)


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [beecoder-220m-python.fp16.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.fp16.gguf) | fp16 | 436.50 MB  |
| [beecoder-220m-python.q2_k.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.q2_k.gguf) | q2_k | 94.43 MB  |
| [beecoder-220m-python.q3_k_m.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.q3_k_m.gguf) | q3_k_m | 114.65 MB  |
| [beecoder-220m-python.q4_k_m.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.q4_k_m.gguf) | q4_k_m | 137.58 MB  |
| [beecoder-220m-python.q5_k_m.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.q5_k_m.gguf) | q5_k_m | 157.91 MB  |
| [beecoder-220m-python.q6_k.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.q6_k.gguf) | q6_k | 179.52 MB  |
| [beecoder-220m-python.q8_0.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/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](https://gist.github.com/pszemraj/c7738f664a64b935a558974d23a7aa8c)

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](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/bLrtpr7Vi_MPvtF7mozDN.png)

The screenshot is on CPU on a laptop.

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