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---
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# pip-code-bandit
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[
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[colab_notebook](https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing)
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Given a goal and tools, can AI intelligently use the tools to reach the goal?\
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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?\
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Releasing **pip-code-bandit** and **pipflow**\
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A `model` and a `library` to manage and run goal
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## Model attributes
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```
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## How we
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We used a simulator to simulate environments where the model could play games to achieve goals, given a set of actions available to it.
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All the model could do was find the right action and config to incur positive reward.
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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.
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In this
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## License
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```bash
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complete open
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```
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## Usage
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-d 'model_name=PipableAI%2Fpip-code-bandit&prompt="YOUR PROMPT"&max_new_tokens=400'
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```
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Alternatively, you can directly access UI endpoint at https://playground.pipable.ai/docs#/default/infer_infer_post.
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### Library Usage
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For detailed usage refer to the [colab_notebook](https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing)
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---
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# pip-code-bandit
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[PipableAI](https://www.pipable.ai/)
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[colab_notebook](https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing)
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Given a goal and tools, can AI intelligently use the tools to reach the goal?\
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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?\
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It can!\
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Releasing **pip-code-bandit** and **pipflow**\
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A `model` and a `library` to manage and run goal-oriented agentic system.
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## Model attributes
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```
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## How did we build it?
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We used a simulator to simulate environments where the model could play games to achieve goals, given a set of actions available to it.
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All the model could do was find the right action and config to incur a positive reward.
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The reward policy is around the concept of a model going to a stable state of zero net sum reward for both good and bad behaviour.
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In this setup, the model, which was pre-trained on code, function documentation, and similar OS datasets, was RL-tuned for reliability and instruction-following.
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## License
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```bash
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complete open-sourced - apache 2.0. License
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```
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## Usage
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-d 'model_name=PipableAI%2Fpip-code-bandit&prompt="YOUR PROMPT"&max_new_tokens=400'
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```
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Alternatively, you can directly access the UI endpoint at https://playground.pipable.ai/docs#/default/infer_infer_post.
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### Library Usage
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To directly use the model's capabilities without putting extra effort into schemas and prompts, try to use [pipflow](https://github.com/PipableAI/pipflow).
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For detailed usage, refer to the [colab_notebook](https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing)
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