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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 0.5.0
 
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  library_name: peft
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+ datasets:
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+ - databricks/databricks-dolly-15k
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+ tags:
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+ - mistral
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+ - databricks
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+ - dolly
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+ - mistral 7b
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+ - llama
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+ - finetune
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+ - finetuning
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  ---
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  ## Training procedure
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+ We finetuned [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) Dataset for 1 epoch using [MonsterAPI](https://monsterapi.ai/) no-code [LLM finetuner](https://monsterapi.ai/finetuning).
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+ ## Finetuning with MonsterAPI no-code LLM Finetuner in 5 easy steps:
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+ 1. **Select an LLM:** Mistral 7B v0.1
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+ 2. **Select a task and Dataset:** Instruction Finetuning and databricks-dolly-15k Dataset
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+ 3. **Specify Hyperparameters:** We used default values suggested by finetuner
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+ 4. **Review and submit the job:** That's it!
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+
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+
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+ ### Hyperparameters & Run details:
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+ - Model: mistralai/Mistral-7B-v0.1
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+ - Dataset: databricks/databricks-dolly-15k
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+ - Learning rate: 0.0002
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+ - Number of epochs: 1
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+ - Cutoff length: 512
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+ - Data split: Training: 95% / Validation: 5%
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+ - Gradient accumulation steps: 1
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+
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+ ### About Model:
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+ The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on majority of the benchmarks as tested by Mistral team.
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+
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+ ### About Dataset:
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+ databricks-dolly-15k is a corpus of more than 15,000 records generated by thousands of Databricks employees to enable large language models to exhibit the magical interactivity of ChatGPT.
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+
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  ### Framework versions
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+ - PEFT 0.5.0