Instructions to use moncefem/salamandra-7b-legal-econ-fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moncefem/salamandra-7b-legal-econ-fr with Transformers:

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

pipe = pipeline("text-generation", model="moncefem/salamandra-7b-legal-econ-fr")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("moncefem/salamandra-7b-legal-econ-fr")
model = AutoModelForMultimodalLM.from_pretrained("moncefem/salamandra-7b-legal-econ-fr")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use moncefem/salamandra-7b-legal-econ-fr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moncefem/salamandra-7b-legal-econ-fr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moncefem/salamandra-7b-legal-econ-fr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/moncefem/salamandra-7b-legal-econ-fr

SGLang

How to use moncefem/salamandra-7b-legal-econ-fr 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 "moncefem/salamandra-7b-legal-econ-fr" \
    --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": "moncefem/salamandra-7b-legal-econ-fr",
		"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 "moncefem/salamandra-7b-legal-econ-fr" \
        --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": "moncefem/salamandra-7b-legal-econ-fr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use moncefem/salamandra-7b-legal-econ-fr with Docker Model Runner:
```
docker model run hf.co/moncefem/salamandra-7b-legal-econ-fr
```

salamandra-7b-legal-econ-fr / model-00004-of-00004.safetensors

moncefem

Upload LlamaForCausalLM

8e3b5b1 verified 6 months ago

Download

History Blame

2.1 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 14bca8a5d757d974b27eedcbc5e3a15fdf1bd1b9b8ddcaaaa02391076a88f505
Size of remote file:: 2.1 GB
SHA256:: c4944914fb33caad0b42c1591478d6fe95dd07811a899ea7ac6b3bfcbd3c2052

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.