Cabrita: closing the gap for foreign languages
Paper β’ 2308.11878 β’ Published β’ 1
How to use 22h/open-cabrita3b with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="22h/open-cabrita3b") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("22h/open-cabrita3b")
model = AutoModelForCausalLM.from_pretrained("22h/open-cabrita3b")How to use 22h/open-cabrita3b with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "22h/open-cabrita3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "22h/open-cabrita3b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/22h/open-cabrita3b
How to use 22h/open-cabrita3b with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "22h/open-cabrita3b" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "22h/open-cabrita3b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "22h/open-cabrita3b" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "22h/open-cabrita3b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use 22h/open-cabrita3b with Docker Model Runner:
docker model run hf.co/22h/open-cabrita3b
The Cabrita model is a collection of continued pre-trained and tokenizer-adapted models for the Portuguese language. This artifact is the 3 billion size variant.
The weights were initially obtained from the open-llama project (https://github.com/openlm-research/open_llama) in the open_llama_3b option.
@misc{larcher2023cabrita,
title={Cabrita: closing the gap for foreign languages},
author={Celio Larcher and Marcos Piau and Paulo Finardi and Pedro Gengo and Piero Esposito and Vinicius CaridΓ‘},
year={2023},
eprint={2308.11878},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 35.54 |
| AI2 Reasoning Challenge (25-Shot) | 33.79 |
| HellaSwag (10-Shot) | 55.35 |
| MMLU (5-Shot) | 25.16 |
| TruthfulQA (0-shot) | 38.50 |
| Winogrande (5-shot) | 59.43 |
| GSM8k (5-shot) | 0.99 |