README.md

---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- python
- java
- cpp
- sql
- function calling
- unit tests
- causalLM
- codeLLAMA modified archi
- document
- code
- code2doc
- instruction_tuned
- basemodel
- pytorch
- docstring
- documentation
- text-generation-inference
- plan
- planner
- llama-cpp
- gguf-my-repo
metrics:
- accuracy
pipeline_tag: text-generation
widget:
- text: '<example_response>--code:def function_divide2(x): return x / 2--question:Document
    the code--doc:Description:This function takes a number and divides it by 2.Parameters:-
    x (numeric): The input value to be divided by 2.Returns:- float: The result of
    x divided by 2.Example:To call the function, use the following code:function_divide2(1.0)</example_response><function_code>def
    _plot_bounding_polygon(polygons_coordinates, output_html_path=bounding_polygon_map.html):map_center
    = [sum([coord[0]for polygon_coords in polygons_coordinatesfor coord in polygon_coords])/
    sum([len(polygon_coords) for polygon_coords in polygons_coordinates]),sum([coord[1]for
    polygon_coords in polygons_coordinatesfor coord in polygon_coords])/ sum([len(polygon_coords)
    for polygon_coords in polygons_coordinates]),]my_map = folium.Map(location=map_center,
    zoom_start=12)for polygon_coords in polygons_coordinates:folium.Polygon(locations=polygon_coords,color=blue,fill=True,fill_color=blue,fill_opacity=0.2,).add_to(my_map)marker_cluster
    = MarkerCluster().add_to(my_map)for polygon_coords in polygons_coordinates:for
    coord in polygon_coords:folium.Marker(location=[coord[0], coord[1]], popup=fCoordinates:
    {coord}).add_to(marker_cluster)draw = Draw(export=True)draw.add_to(my_map)my_map.save(output_html_path)return
    output_html_path</function_code><question>Document the python code above giving
    function description ,parameters and return type and example how to call the function</question><doc>'
  example_title: example
---

# NikolayKozloff/pip-code-bandit-Q8_0-GGUF
This model was converted to GGUF format from [`PipableAI/pip-code-bandit`](https://huggingface.co/PipableAI/pip-code-bandit) 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/PipableAI/pip-code-bandit) for more details on the model.
## Use with llama.cpp

Install llama.cpp through brew.

```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.

CLI:

```bash
llama-cli --hf-repo NikolayKozloff/pip-code-bandit-Q8_0-GGUF --model pip-code-bandit.Q8_0.gguf -p "The meaning to life and the universe is"
```

Server:

```bash
llama-server --hf-repo NikolayKozloff/pip-code-bandit-Q8_0-GGUF --model pip-code-bandit.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.

```
git clone https://github.com/ggerganov/llama.cpp &&             cd llama.cpp &&             make &&             ./main -m pip-code-bandit.Q8_0.gguf -n 128
```