Instructions to use Mirelle/opt-125M-pt-br-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mirelle/opt-125M-pt-br-finetuned with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Mirelle/opt-125M-pt-br-finetuned")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mirelle/opt-125M-pt-br-finetuned")
model = AutoModelForCausalLM.from_pretrained("Mirelle/opt-125M-pt-br-finetuned")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Mirelle/opt-125M-pt-br-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mirelle/opt-125M-pt-br-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mirelle/opt-125M-pt-br-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mirelle/opt-125M-pt-br-finetuned

SGLang

How to use Mirelle/opt-125M-pt-br-finetuned with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Mirelle/opt-125M-pt-br-finetuned" \
    --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": "Mirelle/opt-125M-pt-br-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

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 "Mirelle/opt-125M-pt-br-finetuned" \
        --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": "Mirelle/opt-125M-pt-br-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mirelle/opt-125M-pt-br-finetuned with Docker Model Runner:
```
docker model run hf.co/Mirelle/opt-125M-pt-br-finetuned
```

OPT-125M finetuned Portuguese

Fine-tuning the OPT-125M model on a reduced corpus of mc4-Portuguese with approximately 300M tokens.

Hyper-parameters

learning_rate = 5e-5
batch_size = 32
warmup = 500
seq_length = 512
num_train_epochs = 2.0

With an A100 with 40GB of RAM, the training took around 3 hours

Perplexity: 9.4

Sample Use

from transformers import pipeline
generator = pipeline('text-generation', model='Mirelle/opt-125M-pt-br-finetuned', max_length=100, do_sample=True)
generator("Em uma bela manhã de")

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Mirelle
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opt-125M-pt-br-finetuned

OPT-125M finetuned Portuguese

Hyper-parameters

Sample Use

Dataset used to train Mirelle/opt-125M-pt-br-finetuned