language:
- pt
license: apache-2.0
library_name: transformers
tags:
- text-generation-inference
datasets:
- nicholasKluge/Pt-Corpus-Instruct
metrics:
- perplexity
pipeline_tag: text-generation
widget:
- text: A PUCRS é uma universidade
example_title: Exemplo
- text: A muitos anos atrás, em uma galáxia muito distante, vivia uma raça de
example_title: Exemplo
- text: Em meio a um escândalo, a frente parlamentar pediu ao Senador Silva para
example_title: Exemplo
inference:
parameters:
repetition_penalty: 1.2
temperature: 0.2
top_k: 20
top_p: 0.2
max_new_tokens: 150
co2_eq_emissions:
emissions: 7.6
source: CodeCarbon
training_type: pre-training
geographical_location: Germany
hardware_used: NVIDIA A100-SXM4-40GB
model-index:
- name: Mula-4x160-v0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: ENEM Challenge (No Images)
type: eduagarcia/enem_challenge
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 21.34
name: accuracy
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=MulaBR/Mula-4x160-v0.1
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BLUEX (No Images)
type: eduagarcia-temp/BLUEX_without_images
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 25.17
name: accuracy
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=MulaBR/Mula-4x160-v0.1
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: OAB Exams
type: eduagarcia/oab_exams
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 25.06
name: accuracy
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=MulaBR/Mula-4x160-v0.1
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Assin2 RTE
type: assin2
split: test
args:
num_few_shot: 15
metrics:
- type: f1_macro
value: 33.57
name: f1-macro
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=MulaBR/Mula-4x160-v0.1
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Assin2 STS
type: eduagarcia/portuguese_benchmark
split: test
args:
num_few_shot: 15
metrics:
- type: pearson
value: 11.35
name: pearson
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=MulaBR/Mula-4x160-v0.1
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: FaQuAD NLI
type: ruanchaves/faquad-nli
split: test
args:
num_few_shot: 15
metrics:
- type: f1_macro
value: 43.97
name: f1-macro
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=MulaBR/Mula-4x160-v0.1
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HateBR Binary
type: ruanchaves/hatebr
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 41.5
name: f1-macro
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=MulaBR/Mula-4x160-v0.1
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: PT Hate Speech Binary
type: hate_speech_portuguese
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 22.99
name: f1-macro
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=MulaBR/Mula-4x160-v0.1
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: tweetSentBR
type: eduagarcia/tweetsentbr_fewshot
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 11.24
name: f1-macro
source:
url: >-
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=MulaBR/Mula-4x160-v0.1
name: Open Portuguese LLM Leaderboard
Mula-4x160-v0.1
Model Summary
Mula is a series of Sparse Mixture of Experts (SMoE) language models, all trained natively in Brazilian Portuguese, designed to help democratize LLMs for low-resource languages.
Mula-4x160-v0.1 is one of our first experiments on pre-training a SMoE, using the Pt-Corpus-Instruct dataset. It has 4 experts per layer and activates 2 for each token.
Future versions of Mula will be trained on an extensively larger Brazilian Portuguese dataset.
Details
- Architecture: a Sparse Mixture of Experts (Mixtral implementation) pre-trained via causal language modeling
- Size: 407,820,288 parameters (only 237,950,976 activated parameters during runtime)
- Context length: 2048 tokens
- Dataset: Pt-Corpus Instruct (6.2B tokens)
- Language: Portuguese
- Training time: ~ 30 hours
- Emissions: 7.6 KgCO2eq (Germany)
- Total energy consumption: 15 kWh
Intended Uses
The primary intended use of Mula-4x160-v0.1 is to research the challenges related to developing language models for low-resource languages. Checkpoints saved during training are intended to provide a controlled setting for performing scientific experiments. You may also further fine-tune and adapt Mula-4x160-v0.1 for deployment, as long as your use is following the Apache 2.0 license. If you decide to use pre-trained Mula-4x160-v0.1 as a basis for your fine-tuned model, please conduct your own risk and bias assessment.
Out-of-scope Use
Mula-4x160-v0.1 is not intended for deployment. It is not a product and should not be used for human-facing interactions.
Mula-4x160-v0.1 models are Brazilian Portuguese language only and are not suitable for translation or generating text in other languages.
Mula-4x160-v0.1 has not been fine-tuned for downstream contexts in which language models are commonly deployed.
Basic usage
Using the pipeline:
from transformers import pipeline
generator = pipeline("text-generation", model="MulaBR/Mula-4x160-v0.1")
completions = generator("Astronomia é a ciência", num_return_sequences=2, max_new_tokens=100)
for comp in completions:
print(f"🤖 {comp['generated_text']}")
Using the AutoTokenizer and AutoModelForCausalLM:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load model and the tokenizer
tokenizer = AutoTokenizer.from_pretrained("MulaBR/Mula-4x160-v0.1", revision='main')
model = AutoModelForCausalLM.from_pretrained("MulaBR/Mula-4x160-v0.1", revision='main')
# Pass the model to your device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.eval()
model.to(device)
# Tokenize the inputs and pass them to the device
inputs = tokenizer("Astronomia é a ciência", return_tensors="pt").to(device)
# Generate some text
completions = model.generate(**inputs, num_return_sequences=2, max_new_tokens=100)
# Print the generated text
for i, completion in enumerate(completions):
print(f'🤖 {tokenizer.decode(completion)}')
Limitations
Like almost all other language models trained on large text datasets scraped from the web, Mula-4x160-v0.1 exhibits behavior that does not make them an out-of-the-box solution to many real-world applications, especially those requiring factual, reliable, nontoxic text generation. Our models are all subject to the following:
Hallucinations: This model can produce content that can be mistaken for truth but is, in fact, misleading or entirely false, i.e., hallucination.
Biases and Toxicity: This model inherits the social and historical stereotypes from the data used to train it. Given these biases, the model can produce toxic content, i.e., harmful, offensive, or detrimental to individuals, groups, or communities.
Unreliable Code: The model may produce incorrect code snippets and statements. These code generations should not be treated as suggestions or accurate solutions.
Language Limitations: The model is primarily designed to understand standard Brazilian Portuguese. Other languages might challenge its comprehension, leading to potential misinterpretations or errors in response.
Repetition and Verbosity: The model may get stuck on repetition loops (especially if the repetition penalty during generations is set to a meager value) or produce verbose responses unrelated to the prompt it was given.
Hence, even though our models are released with a permissive license, we urge users to perform their risk analysis on these models if intending to use them for real-world applications and also have humans moderating the outputs of these models in applications where they will interact with an audience, guaranteeing users are always aware they are interacting with a language model.
Benchmarks
Evaluations on benchmarks were performed using the Language Model Evaluation Harness (by EleutherAI). Laiviet translated the tasks from the LM-Evaluation-Harness we used.
| ARC | HellaSwag | MMLU | TruthfulQA | |
|---|---|---|---|---|
| Mula-4x160-v0.1 | 27.09 | 31.41 | 28.15 | 39.81 |
| Mula-8x160-v0.1 | 26.15 | 33.06 | 28.14 | 41.69 |
Evaluations on Brazilian Portuguese benchmarks were performed using a Portuguese implementation of the EleutherAI LM Evaluation Harness (created by Eduardo Garcia).
| ASSIN2 RTE | ASSIN2 STS | BLUEX | ENEM | FAQUAD NLI | HateBR | PT Hate Speech | OAB Exams | TweetSentBR | |
|---|---|---|---|---|---|---|---|---|---|
| Mula-4x160-v0.1 | 33.57 | 11.35 | 25.17 | 21.34 | 43.97 | 41.50 | 22.99 | 25.06 | 11.24 |
| Mula-8x160-v0.1 | 33.51 | 0 | 20.17 | 19.94 | 43.97 | 33.33 | 42.69 | 24.37 | 24.60 |
Cite as 🤗
@misc{mula2024BR,
title = {Mula: a Sparse Mixture of Experts Language Model trained in Brazilian Portuguese},
author = {Corr{\^e}a, Nicholas Kluge and Sen, Aniket and Falk, Sophia and Fatimah, Shiza},
howpublished = {\url{https://huggingface.co/MulaBR}},
year={2024}
}
License
Mula-4x160-v0.1 is licensed under the Apache License, Version 2.0. See the LICENSE file for more details.
Acknowledgements
The authors gratefully acknowledge the granted access to the Marvin cluster hosted by the University of Bonn along with the support provided by its High Performance Computing & Analytics Lab.