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
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
---
# pip-library-etl-1.3b

[pipableAi](https://www.pipable.ai/)

[colab_notebook](https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing)

[pipflow](https://github.com/PipableAI/pipflow)

[linkedin_post]()

[reddit_post]()


## Objective


![image/png](https://cdn-uploads.huggingface.co/production/uploads/658d8095a2a6a6e0da8bb8a6/NuTFBTMAsPgFwMxCjdqFv.png)


Given a goal and tools can Ai intelligently use the tools to reach the goal ?
What if it has a meagre 1.3b params/neurons akin to that of an owl ? 
Can it follow instructions and plan to reach a goal ? 
Apparently it can. 
Releasing `pip-code-bandit` and `pipflow` 
-- a model and a library to manage and run goal oriented agentic system.


## Model attributes

```javascript
-- number of params ~ 1.3b [2.9 Gb GPU memory footprint]
-- sequence length ~ 16.3k [Can go higher but will show performance degradation]
-- license - apache 2.0
-- tasks:
1. complex planning of sequential function calls with right params to accomplish a goal | a list of callables
2. function calling | doc or code and goal
3. code generation | plan and goal
4. code generation | goal
5. doc generation | code
6. code generation | doc
7. file recreated in json | any raw data
8. corrected generation | new instruction with error

-- instruction following , RL tuned.
```


## How we built it?

We used a simulator to simulate environments where the model could play games to achieve goals, given a set of actions available to it. 
All the model could do was find the right action and config to incur positive reward.
The reward policy is around the concept of model going to a stable state of zero net sum reward for both good and bad behaviour.
In this set up the model, which was pre trained on code , function documentation and similar OS datasets ,was RL tuned for instruction following and reliability. 

## License
```bash
complete open sourced - apache 2.0. License
```

## Usage


### NOTE:



### Library use

For directly using the capabilities of model without putting extra efforts on schems and prompts try to use [pipflow](https://github.com/PipableAI/pipflow).

For detailed usage refer to the [colab_notebook](https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing)



### Model Use

```bash
pip install transformers
pip install accelerate
```

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from accelerate import Accelerator
model =AutoModelForCausalLM.from_pretrained("PipableAI/pip-code-bandit",torch_dtype=torch.bfloat16,device_map="auto")
tokenizer = tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-code-bandit")

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=new_tokens,)
out = (
    tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
)
```


### Prompt




### Team

```doc
Avi Kothari, Gyan Ranjan, Pratham Gupta, Ritvik Aryan Kalra, Soham Acharya
```