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Hi folks , 
Hi folks , 
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colab[https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing]
colab[https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing]
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#OpenSourcing
#OpenSourcing
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pip-code-bandit
pip-code-bandit
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-- a model to act as intelligence unit in agentic workflows.
-- a model to act as intelligence unit in agentic workflows.
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pipflow
pipflow
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-- a library to manage and run goal oriented agentic system.
-- a library to manage and run goal oriented agentic system.
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pip-code-bandit attributes-
pip-code-bandit attributes-
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-- number of params ~ 1.3b [2.9 Gb GPU memory footprint]
-- number of params ~ 1.3b [2.9 Gb GPU memory footprint]
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-- sequence length ~ 16.3k [Can go higher but will show performance degradation]
-- sequence length ~ 16.3k [Can go higher but will show performance degradation]
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-- license - apache 2.0
-- license - apache 2.0
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-- instruction following , RL tuned.
-- instruction following , RL tuned.
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-- tasks:
-- tasks:
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complex planning(plan) of sequential function calls | a list of callables and goal
complex planning(plan) of sequential function calls | a list of callables and goal
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corrected plan | feedback instructions with error
corrected plan | feedback instructions with error
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function calling | doc or code and goal
function calling | doc or code and goal
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code generation | plan and goal
code generation | plan and goal
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code generation | goal
code generation | goal
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doc generation | code
doc generation | code
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code generation | doc
code generation | doc
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file parsed to json | any raw data
file parsed to json | any raw data
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sql generation | schema, question, instructions and examples
sql generation | schema, question, instructions and examples
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#Strategy
#Strategy
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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. All the model could do was find the right action and config to incur a positive reward. 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. 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.
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 a positive reward. 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. 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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Do try it out and let me know how its working for you.
Do try it out and let me know how its working for you.
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Thank you :)
Thank you :)
Hi folks , 

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

#OpenSourcing
pip-code-bandit
-- a model to act as intelligence unit in agentic workflows.

pipflow
-- a library to manage and run goal oriented agentic system.

pip-code-bandit attributes-
-- 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
-- instruction following , RL tuned.
-- tasks:
complex planning(plan) of sequential function calls | a list of callables and goal
corrected plan | feedback instructions with error
function calling | doc or code and goal
code generation | plan and goal
code generation | goal
doc generation | code
code generation | doc
file parsed to json | any raw data
sql generation | schema, question, instructions and examples

#Strategy

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 a positive reward. 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. 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.

Do try it out and let me know how its working for you.

Thank you :)
👍
avi-pipable Qagent
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CRYPTOKILLA Qagent
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Qagent