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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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## 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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### 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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### 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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### 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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### Framework versions
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- PEFT 0.5.0
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