Instructions to use monilouise/opt125M_portuguese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use monilouise/opt125M_portuguese with Transformers:

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

pipe = pipeline("text-generation", model="monilouise/opt125M_portuguese")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("monilouise/opt125M_portuguese")
model = AutoModelForCausalLM.from_pretrained("monilouise/opt125M_portuguese", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use monilouise/opt125M_portuguese with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "monilouise/opt125M_portuguese"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "monilouise/opt125M_portuguese",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/monilouise/opt125M_portuguese

SGLang

How to use monilouise/opt125M_portuguese 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 "monilouise/opt125M_portuguese" \
    --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": "monilouise/opt125M_portuguese",
		"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 "monilouise/opt125M_portuguese" \
        --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": "monilouise/opt125M_portuguese",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use monilouise/opt125M_portuguese with Docker Model Runner:
```
docker model run hf.co/monilouise/opt125M_portuguese
```

Model Card for Model ID

A Portuguese language model trained on https://huggingface.co/facebook/opt-125m .

Model Details

Model Description

Developed by: Monique Monteiro
Shared by [optional]: Monique Monteiro
Model type: OPT
Language(s) (NLP): Portuguese
License: [More Information Needed]
Finetuned from model [optional]: facebook/opt-125m

Use the code below to get started with the model.

generator = pipeline('text-generation', 'monilouise/opt125M_portuguese')
output = generator("Era uma vez", max_length=50, do_sample=True)

Training Details

Training Data

The model was trained on gs://unicamp-dl/ia025a_2022s1/aula9/sample-1gb.txt

Training Procedure

The model was trained for 3 epochs, by using learning rate = 5e-5 (linear scheduler).

Preprocessing [optional]

All text was tokenized and broken into chunks of 1024 tokens.

Training Hyperparameters

Training regime: fp16 mixed precision

Speeds, Sizes, Times [optional]

Training time: 17 hours

Evaluation

The model was evaluated on a 5% validation split.

Metrics

Perplexity = 7.94.

Model Card Authors [optional]

moniquelouise@gmail.com

Model Card Contact