Instructions to use semantixai/Lloro-SQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use semantixai/Lloro-SQL with Transformers:

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

pipe = pipeline("text-generation", model="semantixai/Lloro-SQL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("semantixai/Lloro-SQL")
model = AutoModelForCausalLM.from_pretrained("semantixai/Lloro-SQL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use semantixai/Lloro-SQL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "semantixai/Lloro-SQL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "semantixai/Lloro-SQL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/semantixai/Lloro-SQL

SGLang

How to use semantixai/Lloro-SQL 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 "semantixai/Lloro-SQL" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "semantixai/Lloro-SQL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "semantixai/Lloro-SQL" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "semantixai/Lloro-SQL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use semantixai/Lloro-SQL with Docker Model Runner:
```
docker model run hf.co/semantixai/Lloro-SQL
```

rafaelgeraldini

Ivyna commited on May 22, 2024

Commit

63b8988

verified ·

1 Parent(s): 133ccba

edit datasets - intro (#3)

Browse files

- edit datasets - intro (3ef8ab9799ae558d3adc503380d6e9de828ff8a9)

Co-authored-by: Alves <Ivyna@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -15,7 +15,7 @@ tags:
 <img src="https://cdn-uploads.huggingface.co/production/uploads/653176dc69fffcfe1543860a/h0kNd9OTEu1QdGNjHKXoq.png" width="300" alt="Lloro-7b Logo"/>
-Lloro SQL, developed by Semantix Research Labs, is a language Model that was trained to effectively transform Portuguese queries into SQL Code. It is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct,  that was trained on Bird and Spider public datasets.  The fine-tuning process was performed using the QLORA metodology on a GPU A100 with 40 GB of RAM.


15	<img src="https://cdn-uploads.huggingface.co/production/uploads/653176dc69fffcfe1543860a/h0kNd9OTEu1QdGNjHKXoq.png" width="300" alt="Lloro-7b Logo"/>
16
17
18	+ Lloro SQL, developed by Semantix Research Labs, is a language Model that was trained to effectively transform Portuguese queries into SQL Code. It is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct, that was trained on GretelAI public datasets. The fine-tuning process was performed using the QLORA metodology on a GPU A100 with 40 GB of RAM.
19
20
21