---
language:
- ca
- es
- en
license: apache-2.0
library_name: transformers
tags:
- finetune
- chatml
- gpt4
- catalan
datasets:
- xaviviro/oasst2_ca_gpt
- xaviviro/oasst2_es_gpt
base_model: openlm-research/open_llama_3b_v2
widget:
- text: "<|im_start|>user\n Qui va ser Isaac Newton?<|im_end|>\n<|im_start|>assistant\n"
- text: "<|im_start|>user\n ¿Quién fue Isaac Newton?<|im_end|>\n<|im_start|>assistant\n"
model-index:
- name: FLAMA-0.5-3B
  results: []
---

# FLAMA: Model 3B ChatML en Català i Castellà. Versió 0.5


![FLAMA](flama05.png)



👉🏻 [Format GGUF i quantitzat](/xaviviro/FLAMA-0.5-3B-GGUF)


FLAMA és el primer model petit 3B bilingüe en català i castellà. És el resultat de finetunejar el model [open_llama_3b_v2](/openlm-research/open_llama_3b_v2) amb les instruccions d'[OpenAssistant v2](/datasets/OpenAssistant/oasst2) traduïdes automàticament al català i al castellà amb recursos de [Helsinki-NLP](/Helsinki-NLP) i tractades en format ChatML.


## Novetats de la versió 0.5

1. Català millorat
1. Afegit el Castellà



# Prompt Template

FLAMA usa ChatML com a prompt template:

```
<|im_start|>user
Qui va ser Isaac Newton?<|im_end|>
<|im_start|>assistant\n
```
```
<|im_start|>user
Quien fué Isaac Newton?<|im_end|>
<|im_start|>assistant\n
```

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

## Referències

```
@software{xaviviro2023flama,
  author = {xaviviro},
  title = {FLAMA: Model 3B ChatML en Català. Versió 0.5},
  month = January,
  year = 2024,
  url = {https://huggingface.co/xaviviro/FLAMA-0.5-3B}
}
```

```
@software{openlm2023openllama,
  author = {Geng, Xinyang and Liu, Hao},
  title = {OpenLLaMA: An Open Reproduction of LLaMA},
  month = May,
  year = 2023,
  url = {https://github.com/openlm-research/open_llama}
}
```
```
@software{together2023redpajama,
  author = {Together Computer},
  title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset},
  month = April,
  year = 2023,
  url = {https://github.com/togethercomputer/RedPajama-Data}
}
```
```
@article{touvron2023llama,
  title={Llama: Open and efficient foundation language models},
  author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others},
  journal={arXiv preprint arXiv:2302.13971},
  year={2023}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_xaviviro__FLAMA-0.5-3B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |39.23|
|AI2 Reasoning Challenge (25-Shot)|37.97|
|HellaSwag (10-Shot)              |67.65|
|MMLU (5-Shot)                    |25.73|
|TruthfulQA (0-shot)              |41.11|
|Winogrande (5-shot)              |62.12|
|GSM8k (5-shot)                   | 0.83|