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  library_name: peft
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  ## Training procedure
 
 
 
 
 
 
 
 
 
 
 
 
 
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- ### Framework versions
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  - PEFT 0.4.0
 
 
 
 
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  ---
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  library_name: peft
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+ license: apache-2.0
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+ datasets:
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+ - iamtarun/python_code_instructions_18k_alpaca
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+ tags:
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+ - falcon
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+ - falcon-7b
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+ - code
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+ - code instruct
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+ - instruct code
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+ - code alpaca
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+ - python code
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+ - code copilot
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+ - copilot
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+ - python coding assistant
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+ - coding assistant
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  ---
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  ## Training procedure
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+ We finetuned Falcon-7B LLM on Python-Code-Instructions Dataset ([iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca)) for 10 epochs or ~ 23,000 steps using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm).
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+ The dataset contains problem descriptions and code in python language. This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style.
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+ The finetuning session got completed in 7.3 hours and costed us only `$17.5` for the entire finetuning run!
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+
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+ #### Hyperparameters & Run details:
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+ - Model Path: tiiuae/falcon-7b
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+ - Dataset: iamtarun/python_code_instructions_18k_alpaca
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+ - Learning rate: 0.0002
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+ - Number of epochs: 10
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+ - Data split: Training: 95% / Validation: 5%
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+ - Gradient accumulation steps: 1
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+ ### Framework versions
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+
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  - PEFT 0.4.0
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+ ### Loss metrics:
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+ ![training loss](train-loss.png "Training loss")