Instructions to use LarkAI/codet5p-770m_nl2sql_oig with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LarkAI/codet5p-770m_nl2sql_oig with Transformers:

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

pipe = pipeline("text-generation", model="LarkAI/codet5p-770m_nl2sql_oig")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig")
model = AutoModelForSeq2SeqLM.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LarkAI/codet5p-770m_nl2sql_oig with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LarkAI/codet5p-770m_nl2sql_oig"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LarkAI/codet5p-770m_nl2sql_oig",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LarkAI/codet5p-770m_nl2sql_oig

SGLang

How to use LarkAI/codet5p-770m_nl2sql_oig 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 "LarkAI/codet5p-770m_nl2sql_oig" \
    --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": "LarkAI/codet5p-770m_nl2sql_oig",
		"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 "LarkAI/codet5p-770m_nl2sql_oig" \
        --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": "LarkAI/codet5p-770m_nl2sql_oig",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LarkAI/codet5p-770m_nl2sql_oig with Docker Model Runner:
```
docker model run hf.co/LarkAI/codet5p-770m_nl2sql_oig
```

JacksonLark commited on May 23, 2023

Commit

e987fab

1 Parent(s): 922603b

Update README.md

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README.md +3 -3

README.md CHANGED Viewed

@@ -11,7 +11,7 @@ tags:
 # How to Use
-```
 import torch
 from transformers import T5ForConditionalGeneration, AutoTokenizer
@@ -20,9 +20,9 @@ device = torch.device("cuda:0")
 tokenizer = AutoTokenizer.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig")
 model = T5ForConditionalGeneration.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig").to(device)
-text = "Given the following schema:\nroad (road_name, state_name)\nstate (state_name, capital, population, area, country_name, density)\nhighlow (state_name, highest_point, highest_elevation, lowest_point, lowest_elevation)\nlake (lake_name, area, state_name, country_name)\nriver (river_name, length, traverse, country_name)\nborder_info (state_name, border)\nmountain (mountain_name, mountain_altitude, state_name, country_name)\ncity (city_name, state_name, population, country_name)\nWrite a SQL query to what states does the mississippi river run throug"
 inputs = tokenizer.encode(text, return_tensors="pt").to(device)
 output_ids = model.generate(inputs, max_length=512)
 response_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-# SELECT traverse FROM river WHERE river_name = \"mississippi\" ;
 ```

 # How to Use
+```python
 import torch
 from transformers import T5ForConditionalGeneration, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig")
 model = T5ForConditionalGeneration.from_pretrained("LarkAI/codet5p-770m_nl2sql_oig").to(device)
+text = "Given the following schema:\ntrack (Track_ID, Name, Location, Seating, Year_Opened)\nrace (Race_ID, Name, Class, Date, Track_ID)\nWrite a SQL query to count the number of tracks."
 inputs = tokenizer.encode(text, return_tensors="pt").to(device)
 output_ids = model.generate(inputs, max_length=512)
 response_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+# SELECT COUNT( * ) FROM track
 ```