Instructions to use willyninja30/ARIA-70B-French with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use willyninja30/ARIA-70B-French with Transformers:

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

pipe = pipeline("text-generation", model="willyninja30/ARIA-70B-French")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("willyninja30/ARIA-70B-French")
model = AutoModelForCausalLM.from_pretrained("willyninja30/ARIA-70B-French")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use willyninja30/ARIA-70B-French with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "willyninja30/ARIA-70B-French"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "willyninja30/ARIA-70B-French",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/willyninja30/ARIA-70B-French

SGLang

How to use willyninja30/ARIA-70B-French 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 "willyninja30/ARIA-70B-French" \
    --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": "willyninja30/ARIA-70B-French",
		"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 "willyninja30/ARIA-70B-French" \
        --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": "willyninja30/ARIA-70B-French",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use willyninja30/ARIA-70B-French with Docker Model Runner:
```
docker model run hf.co/willyninja30/ARIA-70B-French
```

ARIA French is a model created from llama 2 70B finetuned on a french dataset.

contact@faradaylab.fr

Model Developers :FARADAY

ARIA is the first version of our models based on Llama 2-70B-Chat-HF. We finetuned llama 2 over 10.000 high quality french tokens. This version has been trained on a small dataset extract from the french parliament.

The goal is to increase model quality on French and general topics.

Aria 70B is based on Llama 2-70B-Chat-HF

Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.

*FINETUNING PROCESS **

We trained the model on a high quality dataset with more than 50.000 rows of french language. The training took 2 days on Amazon Cloud Sagemaker powered by Nvidia GPUs.

Timing of training

1 Day using NVIDIA A100 and a cloud service. We are grateful to Nvidia Inception program.

We are also applying rope scalling as experimental approach used by several other Open source teams to increase context lenght of ARIA from 4,096 to over 6,000 tokens. This will allow the model to handle large files for data extraction. This is not active by default and you should add a line of code at parameters to activate rope scaling.

Model Details /

Note: Use of this model is governed by the Meta license because it's based on LLAMA 2. In order to download the model weights and tokenizer, please visit the website and accept our License before requesting access here.

Variations :ARIA comes in a range of parameter sizes — 7B, 40B (based on Falcon), and 70B finetuned on French language datasets.

Input :Models input text only.

Output : Models generate text only.

Model Architecture : ARIA is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.

License : A custom commercial license is available at: https://ai.meta.com/resources/models-and-libraries/llama-downloads/

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ARIA-70B-French