File size: 4,232 Bytes
2c64be0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
---
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.
The screenshot is on CPU on a laptop.
--- |