README.md

---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: SeaLLMs/SeaLLM-7B-v2
datasets:
- 922-Narra/synthetic_tagalog_test_02102024
---
# AKA Tagamistral-7b-v1:
* Yet another archived test/toy model, fine-tuned on a synthetic Tagalog dataset partially produced by [Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) based on [this dataset](https://huggingface.co/datasets/jfernandez/cebuano-filipino-sentences)
* Base: [SeaLLM](https://huggingface.co/SeaLLMs/SeaLLM-7B-v2)
* [GGUF](https://huggingface.co/922-Narra/tagalog-seallm-7b-v1-gguf)

### USAGE
This is meant to be mainly a chat model.

Best results with "Human" and "Assistant" and prompt with Tagalog. Example:

"Ito ay isang chat log sa pagitan ng AI Assistant na nagta-Tagalog at isang Pilipino. Magsimula ng chat:\nHuman: Hello po?\nAssistant:"

### HYPERPARAMS
* Trained for ~1 epoch
* rank: 32
* lora alpha: 32
* lora dropout: 0
* lr: 2e-4
* batch size: 2
* grad steps: 4

This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

### WARNINGS AND DISCLAIMERS
There is still a chance that the model may switch to English or Taglish.

It is possible that the Tagalog capability still comes mostly from the fine-tuned base more than the dataset.

Finally, this model is not guaranteed to output aligned or safe outputs nor is it meant for production use - use at your own risk!