Instructions to use somosnlp/LLM_SQL_BaseDatosEspanol_Mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use somosnlp/LLM_SQL_BaseDatosEspanol_Mistral with Transformers:

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

pipe = pipeline("text-generation", model="somosnlp/LLM_SQL_BaseDatosEspanol_Mistral")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("somosnlp/LLM_SQL_BaseDatosEspanol_Mistral")
model = AutoModelForCausalLM.from_pretrained("somosnlp/LLM_SQL_BaseDatosEspanol_Mistral", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use somosnlp/LLM_SQL_BaseDatosEspanol_Mistral with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/somosnlp/LLM_SQL_BaseDatosEspanol_Mistral

SGLang

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

Docker Model Runner
How to use somosnlp/LLM_SQL_BaseDatosEspanol_Mistral with Docker Model Runner:
```
docker model run hf.co/somosnlp/LLM_SQL_BaseDatosEspanol_Mistral
```

Asis41 commited on Apr 11, 2024

Commit

c6f303b

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1 Parent(s): 770579c

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@@ -35,11 +35,6 @@ Como segundo modelo se utilizo Mistral7b este se hizo un fine-tuning con el mode
 - [Mistral](https://github.com/Asis41/LLM_SQL_BaseDatosEspanol/blob/main/trainer-llm-detect-ai-comp-mistral-7b%20(1).ipynb)
-#### Restulados
-Todo esto con el fin de poder ver cual modelo da mejores rstulados para ver cual hacia un mejor ajuste pese a la poca cantidad de datos que se esperan aumentar
-en un futuro
 ## Contribuciones
 Nuestras contribuciones se listan a continuación:

 - [Mistral](https://github.com/Asis41/LLM_SQL_BaseDatosEspanol/blob/main/trainer-llm-detect-ai-comp-mistral-7b%20(1).ipynb)
 ## Contribuciones
 Nuestras contribuciones se listan a continuación: