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---
library_name: peft
datasets:
- databricks/databricks-dolly-15k
tags:
- mistral
- databricks
- dolly
- mistral 7b
- llama
- finetune
- finetuning
---
## Training procedure
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).
## Finetuning with MonsterAPI no-code LLM Finetuner in 5 easy steps:
1. **Select an LLM:** Mistral 7B v0.1
2. **Select a task and Dataset:** Instruction Finetuning and databricks-dolly-15k Dataset
3. **Specify Hyperparameters:** We used default values suggested by finetuner
4. **Review and submit the job:** That's it!
### Hyperparameters & Run details:
- Model: mistralai/Mistral-7B-v0.1
- Dataset: databricks/databricks-dolly-15k
- Learning rate: 0.0002
- Number of epochs: 1
- Cutoff length: 512
- Data split: Training: 95% / Validation: 5%
- Gradient accumulation steps: 1
### About Model:
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.
### About Dataset:
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.
### Framework versions
- PEFT 0.5.0 |