Train openAI curie model using the data sample of at least a 100 elements

[kislerdm]

Problem

OpenAI provides LLMs which however do not perform well when it comes to text2diagram.

Proposed Solution

Perform fine-tuning of the curie model using the data sample of 100+ elements:

• Training sample of 100+ data points will be collected as json file with the structure:

[{
     "prompt": "three connected boxes",
     "completion": {"nodes":[{"id":"0"},{"id":"1"},{"id":"2"}],"links":[{"from":"0","to":"1"},{"from":"1","to":"2"},{"from":"2","to":"0"}]}
},
...
]

• The training will be submitted following the instructions. See the reference for details.
• The resulting model will have the suffix of the commit sha corresponding to the *.py file with the training data sample.

DoD

• The model is successfully trained

Acceptance Criteria

• The model is available for inference
• The model performance, i.e. training metrics are noted

[kislerdm]

@deim @diegohordi @ColeDrain @UtopicUnicorn Hey folks! Your help is crucial here! Please submit your examples to the directory, feel free to use the sample as the structure example.