---
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):\n    return\n"
  example_title: Add Numbers Function
- 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: 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):\n    if n == 0:\n        return 1\n    else:\n"
  example_title: Factorial 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: 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:\n    return\n"
  example_title: Reverse String Function
- text: "def is_palindrome(word:str) -> bool:\n    return\n"
  example_title: Palindrome 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: Bubble Sort 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"
  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`](https://huggingface.co/BEE-spoke-data/beecoder-220M-python) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/BEE-spoke-data/beecoder-220M-python) for more details on the model.

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

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
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:
```bash
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](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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"
```
or 
```
./llama-server --hf-repo ysn-rfd/beecoder-220M-python-Q8_0-GGUF --hf-file beecoder-220m-python-q8_0.gguf -c 2048
```