Instructions to use bertin-project/filiberto-124M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bertin-project/filiberto-124M with Transformers:

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

pipe = pipeline("text-generation", model="bertin-project/filiberto-124M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bertin-project/filiberto-124M")
model = AutoModelForCausalLM.from_pretrained("bertin-project/filiberto-124M")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bertin-project/filiberto-124M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bertin-project/filiberto-124M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bertin-project/filiberto-124M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bertin-project/filiberto-124M

SGLang

How to use bertin-project/filiberto-124M 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 "bertin-project/filiberto-124M" \
    --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": "bertin-project/filiberto-124M",
		"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 "bertin-project/filiberto-124M" \
        --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": "bertin-project/filiberto-124M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bertin-project/filiberto-124M with Docker Model Runner:
```
docker model run hf.co/bertin-project/filiberto-124M
```

Filiberto 124M is a small specialized foundation model trained on Spanish Golden Age Dramas.

Filiberto 124M OCR is only 124 million parameters. It can run easily on CPU or provide correction at scale on GPUs (>10k tokens/seconds).

Training

The pre-training material included a collection of works taken from the TEXORO corpus, via a collaboration with ETSO, totalling ~5 million tokens.

Pre-training ran on 5 epochs with levanter (500 steps total, each processing 1024 sequences of 512 tokens) on a TPUv4-32 for 15 minutes.

Tokenization is currently done with the GPT-2 tokenizer.

Downloads last month: 5

Safetensors

Model size

0.1B params

Tensor type

F32

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