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")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

Create README.md

by alvp - opened May 28, 2024

base: refs/heads/main

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from: refs/pr/1

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+---
+license: apache-2.0
+datasets:
+- fistro/gromenauer
+language:
+- es
+pipeline_tag: text-generation
+---
+# Bertin-Gromenauer
+<div align=center>
+<img alt="BERTIN-gromenauer logo" src="https://huggingface.co/bertin-project/bertin-gromenauer/resolve/main/images/gromenauer.png" width="200px">
+</div>
+## Overview
+Bertin-Gromenauer 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
+- **Hidden Dimension**: 4096
+- **Intermediate Dimension**: 14336
+- **Number of Layers**: 32
+- **Number of Attention Heads**: 32
+- **Number of Key-Value Heads**: 8
+- **Activation Function**: SiLU
+- **Initializer Range**: 0.02
+- **Layer Norm Epsilon**: 1.0e-05
+- **Use Flash Attention**: Yes
+- **Gradient Checkpointing**: Enabled (Block Size: 5)
+- **Sliding Window Attention**: 4096
+- **Use Bias**: No
+## Training Details
+- **Tokenizer**: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+- **Batch Size**: 512
+- **Learning Rate**: 1e-5
+- **Optimizer**: Adam with beta1=0.9, beta2=0.95, epsilon=1e-8
+- **Weight Decay**: 0.1
+- **Warmup Steps**: 200
+- **Learning Rate Schedule**: Cosine
+- **Number of Training Steps**: 7000
+## Usage
+To load the model in your project, you can use the following code:
+```python
+from transformers import AutoModel, AutoTokenizer
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("bertin-project/bertin-gromenauer")
+# Load the model
+model = AutoModel.from_pretrained("bertin-project/bertin-gromenauer")
+# Example usage
+text = "Introduce aquí tu texto en español."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)