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", device_map="auto")
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 Settings

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 Jul 30, 2024

Commit

6782b08

verified ·

1 Parent(s): 09a3483

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -0

README.md CHANGED Viewed

@@ -66,6 +66,7 @@ print(outputs[0]["generated_text"][-1])
 ```
 Here are some exciting examples:
 **Example 1:**
 - model input：
   {"role": "system",
@@ -76,6 +77,7 @@ Here are some exciting examples:
 - model output:
 {'role': 'assistant', 'content': 'SELECT T1.cost_of_treatment,  T2.treatment_type_description FROM Treatments AS T1 JOIN treatment_types AS T2 ON T1.treatment_type_code  =  T2.treatment_type_code'}
 **Example 2:**
 - model input：
   {"role": "system",
@@ -87,6 +89,7 @@ Here are some exciting examples:
   {'role': 'assistant', 'content': "SELECT COUNT(*) > 0 FROM microlab WHERE microlab.patientunitstayid IN (SELECT patient.patientunitstayid FROM patient WHERE patient.patienthealthsystemstayid IN (SELECT patient.patienthealthsystemstayid FROM patient WHERE patient.uniquepid = '031-15666')) AND microlab.culturesite = 'urine, catheter specimen' AND DATETIME(microlab.culturetakentime,'start of month') = DATETIME(CURRENT_TIME(),'start of month', '-1 month') ORDER BY microlab.culturetakentime LIMIT 1"}
 ## 3. Ethical Considerations
 While fine-tuned for text to sql, this model inherits the ethical considerations of the base Llama 3 model. Use responsibly and implement additional safeguards as needed for your application.

 ```
 Here are some exciting examples:
 **Example 1:**
 - model input：
   {"role": "system",
 - model output:
 {'role': 'assistant', 'content': 'SELECT T1.cost_of_treatment,  T2.treatment_type_description FROM Treatments AS T1 JOIN treatment_types AS T2 ON T1.treatment_type_code  =  T2.treatment_type_code'}
 **Example 2:**
 - model input：
   {"role": "system",
   {'role': 'assistant', 'content': "SELECT COUNT(*) > 0 FROM microlab WHERE microlab.patientunitstayid IN (SELECT patient.patientunitstayid FROM patient WHERE patient.patienthealthsystemstayid IN (SELECT patient.patienthealthsystemstayid FROM patient WHERE patient.uniquepid = '031-15666')) AND microlab.culturesite = 'urine, catheter specimen' AND DATETIME(microlab.culturetakentime,'start of month') = DATETIME(CURRENT_TIME(),'start of month', '-1 month') ORDER BY microlab.culturetakentime LIMIT 1"}
 ## 3. Ethical Considerations
 While fine-tuned for text to sql, this model inherits the ethical considerations of the base Llama 3 model. Use responsibly and implement additional safeguards as needed for your application.