Instructions to use Payoto/bart-base-finetuned-en-to-ro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Payoto/bart-base-finetuned-en-to-ro with Transformers:

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

pipe = pipeline("text-generation", model="Payoto/bart-base-finetuned-en-to-ro")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Payoto/bart-base-finetuned-en-to-ro")
model = AutoModelForSeq2SeqLM.from_pretrained("Payoto/bart-base-finetuned-en-to-ro")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Payoto/bart-base-finetuned-en-to-ro with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Payoto/bart-base-finetuned-en-to-ro

SGLang

How to use Payoto/bart-base-finetuned-en-to-ro 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 "Payoto/bart-base-finetuned-en-to-ro" \
    --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": "Payoto/bart-base-finetuned-en-to-ro",
		"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 "Payoto/bart-base-finetuned-en-to-ro" \
        --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": "Payoto/bart-base-finetuned-en-to-ro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Payoto/bart-base-finetuned-en-to-ro with Docker Model Runner:
```
docker model run hf.co/Payoto/bart-base-finetuned-en-to-ro
```

bart-base-finetuned-en-to-ro

This model is a fine-tuned version of facebook/bart-base on the wmt16 dataset. It achieves the following results on the evaluation set:

Loss: 1.9824

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: IPU
gradient_accumulation_steps: 128
total_train_batch_size: 512
total_eval_batch_size: 24
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1
training precision: Mixed Precision

Training results

Training Loss	Epoch	Step	Validation Loss
1.4153	1.0	1192	1.9824

Framework versions

Transformers 4.20.1
Pytorch 1.10.0+cpu
Datasets 2.7.1
Tokenizers 0.12.1

Downloads last month: 16

Safetensors

Model size

0.1B params

Tensor type

F16

Payoto
/

bart-base-finetuned-en-to-ro

bart-base-finetuned-en-to-ro

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Dataset used to train Payoto/bart-base-finetuned-en-to-ro