Instructions to use xbrain/AutoSQL-nl2sql-1.0-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xbrain/AutoSQL-nl2sql-1.0-8b with Transformers:

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

pipe = pipeline("text-generation", model="xbrain/AutoSQL-nl2sql-1.0-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xbrain/AutoSQL-nl2sql-1.0-8b")
model = AutoModelForCausalLM.from_pretrained("xbrain/AutoSQL-nl2sql-1.0-8b")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use xbrain/AutoSQL-nl2sql-1.0-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xbrain/AutoSQL-nl2sql-1.0-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xbrain/AutoSQL-nl2sql-1.0-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xbrain/AutoSQL-nl2sql-1.0-8b

SGLang

How to use xbrain/AutoSQL-nl2sql-1.0-8b 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 "xbrain/AutoSQL-nl2sql-1.0-8b" \
    --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": "xbrain/AutoSQL-nl2sql-1.0-8b",
		"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 "xbrain/AutoSQL-nl2sql-1.0-8b" \
        --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": "xbrain/AutoSQL-nl2sql-1.0-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xbrain/AutoSQL-nl2sql-1.0-8b with Docker Model Runner:
```
docker model run hf.co/xbrain/AutoSQL-nl2sql-1.0-8b
```

hjd commited on Aug 1, 2024

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@@ -36,7 +36,7 @@ Test results on multiple benchmark datasets show the model exceeds other existin
 - On the Spider dataset, the model achieved an execution accuracy rate of 95.3%.
 ### 1.4 functions and advantages
-1. **Semantic Understanding**: It can comprehend complex natural language requests and extract key elements such as patient ID, test type, time range, etc.
 2. **Automated Query Generation**: Generates correct and optimized SQL queries based on the database structure and table relationships, eliminating the need for manual code writing.
 3. **Handling Complex Conditions**: Manages nested queries, multi-table joins, and complex time conditions, which might be error-prone or time-consuming if done manually.
 4. **Efficiency**: Quickly generates queries, significantly enhancing productivity, especially in scenarios requiring frequent database queries.

 - On the Spider dataset, the model achieved an execution accuracy rate of 95.3%.
 ### 1.4 functions and advantages
+1. **Semantic Understanding**: It can comprehend complex natural language requests and extract key elements such as ID, test type, time range, etc.
 2. **Automated Query Generation**: Generates correct and optimized SQL queries based on the database structure and table relationships, eliminating the need for manual code writing.
 3. **Handling Complex Conditions**: Manages nested queries, multi-table joins, and complex time conditions, which might be error-prone or time-consuming if done manually.
 4. **Efficiency**: Quickly generates queries, significantly enhancing productivity, especially in scenarios requiring frequent database queries.