metadata

license: apache-2.0
base_model: BEE-spoke-data/beecoder-220M-python
datasets:
  - BEE-spoke-data/pypi_clean-deduped
  - bigcode/the-stack-smol-xl
  - EleutherAI/proof-pile-2
language:
  - en
tags:
  - python
  - codegen
  - markdown
  - smol_llama
  - llama-cpp
  - gguf-my-repo
metrics:
  - accuracy
inference:
  parameters:
    max_new_tokens: 64
    min_new_tokens: 8
    do_sample: true
    epsilon_cutoff: 0.0008
    temperature: 0.3
    top_p: 0.9
    repetition_penalty: 1.02
    no_repeat_ngram_size: 8
    renormalize_logits: true
widget:
  - text: |
      def add_numbers(a, b):
          return
    example_title: Add Numbers Function
  - text: |
      class Car:
          def __init__(self, make, model):
              self.make = make
              self.model = model

          def display_car(self):
    example_title: Car Class
  - text: |
      import pandas as pd
      data = {'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 21, 19]}
      df = pd.DataFrame(data).convert_dtypes()
      # eda
    example_title: Pandas DataFrame
  - text: |
      def factorial(n):
          if n == 0:
              return 1
          else:
    example_title: Factorial 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: Fibonacci Function
  - text: |
      import matplotlib.pyplot as plt
      import numpy as np
      x = np.linspace(0, 10, 100)
      # simple plot
    example_title: Matplotlib Plot
  - text: |
      def reverse_string(s:str) -> str:
          return
    example_title: Reverse String Function
  - text: |
      def is_palindrome(word:str) -> bool:
          return
    example_title: Palindrome 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: Bubble Sort 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:
    example_title: Binary Search Function
pipeline_tag: text-generation

ysn-rfd/beecoder-220M-python-Q8_0-GGUF

This model was converted to GGUF format from BEE-spoke-data/beecoder-220M-python using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo ysn-rfd/beecoder-220M-python-Q8_0-GGUF --hf-file beecoder-220m-python-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo ysn-rfd/beecoder-220M-python-Q8_0-GGUF --hf-file beecoder-220m-python-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo ysn-rfd/beecoder-220M-python-Q8_0-GGUF --hf-file beecoder-220m-python-q8_0.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo ysn-rfd/beecoder-220M-python-Q8_0-GGUF --hf-file beecoder-220m-python-q8_0.gguf -c 2048