Instructions to use EstherT/en-fr_translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EstherT/en-fr_translator with Transformers:

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

pipe = pipeline("text-generation", model="EstherT/en-fr_translator")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("EstherT/en-fr_translator")
model = AutoModelForSeq2SeqLM.from_pretrained("EstherT/en-fr_translator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EstherT/en-fr_translator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/EstherT/en-fr_translator

SGLang

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

Docker Model Runner
How to use EstherT/en-fr_translator with Docker Model Runner:
```
docker model run hf.co/EstherT/en-fr_translator
```

en-fr_translator / README.md

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metadata

language:
  - en
  - fr
tags:
  - translation
  - NLP
datasets:
  - enimai/MuST-C-fr
metrics:
  - sacrebleu
pipeline_tag: text2text-generation

Model Card for Model ID

This model provides the translation of short English sentences into French.

Model Details

Model Description

Model type: Text2Text Generation
Language(s) (NLP): English, French
Finetuned from model: T5-Base

Training Details

Training Data

The data used for traing was a randomized 10000-instance sub-set obtained from the 'train' split of the 'enimai/MuST-C-fr dataset.

Evaluation

The evaluation was made using 'sacrebleu'

Testing Data

The data used for testing was the 2632-instance 'test' split of the 'enimai/MuST-C-fr dataset.