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README.md
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---
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base_model: BEE-spoke-data/beecoder-220M-python
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datasets:
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- BEE-spoke-data/pypi_clean-deduped
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- bigcode/the-stack-smol-xl
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- EleutherAI/proof-pile-2
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inference: false
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language:
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- en
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license: apache-2.0
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metrics:
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- accuracy
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model_creator: BEE-spoke-data
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model_name: beecoder-220M-python
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pipeline_tag: text-generation
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quantized_by: afrideva
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tags:
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- python
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- codegen
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- markdown
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- smol_llama
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- gguf
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- ggml
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- quantized
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- q2_k
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- q3_k_m
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- q4_k_m
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- q5_k_m
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- q6_k
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- q8_0
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widget:
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- example_title: Add Numbers Function
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text: "def add_numbers(a, b):\n return\n"
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- example_title: Car Class
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text: "class Car:\n def __init__(self, make, model):\n self.make = make\n
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\ self.model = model\n\n def display_car(self):\n"
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- example_title: Pandas DataFrame
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text: 'import pandas as pd
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data = {''Name'': [''Tom'', ''Nick'', ''John''], ''Age'': [20, 21, 19]}
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df = pd.DataFrame(data).convert_dtypes()
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# eda
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'
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- example_title: Factorial Function
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text: "def factorial(n):\n if n == 0:\n return 1\n else:\n"
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- example_title: Fibonacci Function
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text: "def fibonacci(n):\n if n <= 0:\n raise ValueError(\"Incorrect input\")\n
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\ elif n == 1:\n return 0\n elif n == 2:\n return 1\n else:\n"
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- example_title: Matplotlib Plot
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text: 'import matplotlib.pyplot as plt
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import numpy as np
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x = np.linspace(0, 10, 100)
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# simple plot
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'
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- example_title: Reverse String Function
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text: "def reverse_string(s:str) -> str:\n return\n"
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- example_title: Palindrome Function
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text: "def is_palindrome(word:str) -> bool:\n return\n"
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- example_title: Bubble Sort Function
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text: "def bubble_sort(lst: list):\n n = len(lst)\n for i in range(n):\n for
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j in range(0, n-i-1):\n"
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- example_title: Binary Search Function
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text: "def binary_search(arr, low, high, x):\n if high >= low:\n mid =
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(high + low) // 2\n if arr[mid] == x:\n return mid\n elif
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arr[mid] > x:\n"
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---
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# BEE-spoke-data/beecoder-220M-python-GGUF
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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)
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [beecoder-220m-python.fp16.gguf](https://huggingface.co/afrideva/beecoder-220M-python-GGUF/resolve/main/beecoder-220m-python.fp16.gguf) | fp16 | 436.50 MB |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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## Original Model Card:
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# BEE-spoke-data/beecoder-220M-python
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This is `BEE-spoke-data/smol_llama-220M-GQA` fine-tuned for code generation on:
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- filtered version of stack-smol-XL
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- deduped version of 'algebraic stack' from proof-pile-2
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- cleaned and deduped pypi (last dataset)
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This model (and the base model) were both trained using ctx length 2048.
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## examples
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> Example script for inference testing: [here](https://gist.github.com/pszemraj/c7738f664a64b935a558974d23a7aa8c)
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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.
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/bLrtpr7Vi_MPvtF7mozDN.png)
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The screenshot is on CPU on a laptop.
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---
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