README.md

metadata

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):
          return
  - example_title: Car Class
    text: |
      class Car:
          def __init__(self, make, model):
              self.make = make
              self.model = model

          def display_car(self):
  - 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):
          if n == 0:
              return 1
          else:
  - example_title: Fibonacci Function
    text: |
      def fibonacci(n):
          if n <= 0:
              raise ValueError("Incorrect input")
          elif n == 1:
              return 0
          elif n == 2:
              return 1
          else:
  - 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:
          return
  - example_title: Palindrome Function
    text: |
      def is_palindrome(word:str) -> bool:
          return
  - example_title: Bubble Sort Function
    text: |
      def bubble_sort(lst: list):
          n = len(lst)
          for i in range(n):
              for j in range(0, n-i-1):
  - example_title: Binary Search Function
    text: |
      def binary_search(arr, low, high, x):
          if high >= low:
              mid = (high + low) // 2
              if arr[mid] == x:
                  return mid
              elif arr[mid] > x:

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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