Instructions to use bertin-project/Gromenauer-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bertin-project/Gromenauer-7B with Transformers:

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

pipe = pipeline("text-generation", model="bertin-project/Gromenauer-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bertin-project/Gromenauer-7B")
model = AutoModelForCausalLM.from_pretrained("bertin-project/Gromenauer-7B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bertin-project/Gromenauer-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bertin-project/Gromenauer-7B"
# 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/Gromenauer-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bertin-project/Gromenauer-7B

SGLang

How to use bertin-project/Gromenauer-7B 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/Gromenauer-7B" \
    --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/Gromenauer-7B",
		"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/Gromenauer-7B" \
        --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/Gromenauer-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bertin-project/Gromenauer-7B with Docker Model Runner:
```
docker model run hf.co/bertin-project/Gromenauer-7B
```

alvp commited on May 28, 2024

Commit

36ddc24

verified ·

1 Parent(s): ac3a3ae

Update model name

Browse files

Files changed (1) hide show

README.md +4 -6

README.md CHANGED Viewed

@@ -9,13 +9,11 @@ pipeline_tag: text-generation
 # Gromenauer-7B
 <div align=center>
-<img alt="gromenauer-7B logo" src="https://huggingface.co/bertin-project/bertin-gromenauer/resolve/main/images/gromenauer.png" width="200px">
 </div>
 ## Overview
-gromenauer-7B is a Spanish language model designed to understand and generate high-quality Spanish text. Developed using the robust Mistral architecture, this model has been trained on an extensive literary corpus, ensuring it captures a wide range of linguistic nuances, styles, and contexts found in Spanish literature.
-## Model Details
 - **Model Type**: Mistral
 - **Sequence Length**: 8192
@@ -51,10 +49,10 @@ To load the model in your project, you can use the following code:
 from transformers import AutoModel, AutoTokenizer
 # Load the tokenizer
-tokenizer = AutoTokenizer.from_pretrained("bertin-project/gromenauer-7B")
 # Load the model
-model = AutoModel.from_pretrained("bertin-project/gromenauer-7B")
 # Example usage
 text = "Introduce aquí tu texto en español."

 # Gromenauer-7B
 <div align=center>
+<img alt="gromenauer-7B logo" src="https://huggingface.co/bertin-project/Gromenauer-7B/resolve/main/images/gromenauer.png" width="200px">
 </div>
 ## Overview
+Gromenauer-7B is a Spanish language model designed to understand and generate high-quality Spanish text. Developed using the robust Mistral architecture, this model has been trained on an extensive literary corpus, ensuring it captures a wide range of linguistic nuances, styles, and contexts found in Spanish literature.## Model Details
 - **Model Type**: Mistral
 - **Sequence Length**: 8192
 from transformers import AutoModel, AutoTokenizer
 # Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("bertin-project/Gromenauer-7B")
 # Load the model
+model = AutoModel.from_pretrained("bertin-project/Gromenauer-7B")
 # Example usage
 text = "Introduce aquí tu texto en español."