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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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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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#### 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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- PEFT 0.4.0
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### Loss metrics:
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![training loss](train-loss.png "Training loss")
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