Instructions to use unsqldotai/unSQLv1-7b-sqlite-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsqldotai/unSQLv1-7b-sqlite-lora with Transformers:

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

pipe = pipeline("text-generation", model="unsqldotai/unSQLv1-7b-sqlite-lora")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("unsqldotai/unSQLv1-7b-sqlite-lora")
model = AutoModelForCausalLM.from_pretrained("unsqldotai/unSQLv1-7b-sqlite-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use unsqldotai/unSQLv1-7b-sqlite-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsqldotai/unSQLv1-7b-sqlite-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsqldotai/unSQLv1-7b-sqlite-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/unsqldotai/unSQLv1-7b-sqlite-lora

SGLang

How to use unsqldotai/unSQLv1-7b-sqlite-lora 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 "unsqldotai/unSQLv1-7b-sqlite-lora" \
    --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": "unsqldotai/unSQLv1-7b-sqlite-lora",
		"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 "unsqldotai/unSQLv1-7b-sqlite-lora" \
        --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": "unsqldotai/unSQLv1-7b-sqlite-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use unsqldotai/unSQLv1-7b-sqlite-lora with Docker Model Runner:
```
docker model run hf.co/unsqldotai/unSQLv1-7b-sqlite-lora
```

pwnshx commited on Mar 21, 2024

Commit

fd7845c

verified ·

1 Parent(s): 48054f0

Update README.md

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README.md +16 -6

README.md CHANGED Viewed

@@ -69,20 +69,30 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ## Sagemaker Endpoint I/O Example
-```js
-{
     "inputs": "### Schema and the Natural Language Query:\nCREATE TABLE stadium (\n    stadium_id number,\n    location text,\n    name text,\n    capacity number,\n    highest number,\n    lowest number,\n    average number\n)\n\nCREATE TABLE singer (\n    singer_id number,\n    name text,\n    country text,\n    song_name text,\n    song_release_year text,\n    age number,\n    is_male others\n)\n\nCREATE TABLE concert (\n    concert_id number,\n    concert_name text,\n    theme text,\n    stadium_id text,\n    year text\n)\n\nCREATE TABLE singer_in_concert (\n    concert_id number,\n    singer_id text\n)\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- What is the maximum, the average, and the minimum capacity of stadiums ?",
     "parameters": {
         "maxNewTokens": 512,
         "topP": 0.9,
-        "temperature": 0.2,
-        "decoderInputDetails": true,
-        "details": true
     }
 }
 ```
-```js
 {
     "body": [
         {

 ## Sagemaker Endpoint I/O Example
+```py
+payload = {
     "inputs": "### Schema and the Natural Language Query:\nCREATE TABLE stadium (\n    stadium_id number,\n    location text,\n    name text,\n    capacity number,\n    highest number,\n    lowest number,\n    average number\n)\n\nCREATE TABLE singer (\n    singer_id number,\n    name text,\n    country text,\n    song_name text,\n    song_release_year text,\n    age number,\n    is_male others\n)\n\nCREATE TABLE concert (\n    concert_id number,\n    concert_name text,\n    theme text,\n    stadium_id text,\n    year text\n)\n\nCREATE TABLE singer_in_concert (\n    concert_id number,\n    singer_id text\n)\n\n-- Using valid SQLite, answer the following questions for the tables provided above.\n\n-- What is the maximum, the average, and the minimum capacity of stadiums ?",
     "parameters": {
         "maxNewTokens": 512,
         "topP": 0.9,
+        "temperature": 0.2
     }
 }
+client = boto3.client('runtime.sagemaker')
+endpoint_name = 'deployed_model_name'
+response = client.invoke_endpoint(
+    EndpointName=endpoint_name,
+    ContentType='application/json',
+    Body=json.dumps(payload).encode('utf-8'),
+)
+response = response["Body"].read().decode("utf8")
+response = json.loads(response)
+print(response[0]['generated_text'])
 ```
+```json
 {
     "body": [
         {