Instructions to use anilajax/text2sql_industry_standard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anilajax/text2sql_industry_standard with Transformers:

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

pipe = pipeline("text-generation", model="anilajax/text2sql_industry_standard")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("anilajax/text2sql_industry_standard")
model = AutoModelForSeq2SeqLM.from_pretrained("anilajax/text2sql_industry_standard")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use anilajax/text2sql_industry_standard with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anilajax/text2sql_industry_standard"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anilajax/text2sql_industry_standard",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/anilajax/text2sql_industry_standard

SGLang

How to use anilajax/text2sql_industry_standard 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 "anilajax/text2sql_industry_standard" \
    --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": "anilajax/text2sql_industry_standard",
		"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 "anilajax/text2sql_industry_standard" \
        --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": "anilajax/text2sql_industry_standard",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use anilajax/text2sql_industry_standard with Docker Model Runner:
```
docker model run hf.co/anilajax/text2sql_industry_standard
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Industry standard text to sql generation with high accuracy.

Sample code to begin with:

import torch from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained('anilajax/text2sql_industry_standard')

device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = T5ForConditionalGeneration.from_pretrained('anilajax/text2sql_industry_standard') model = model.to(device) model.eval()

def generate_sql(input_prompt): # Tokenize the input prompt inputs = tokenizer(input_prompt, padding=True, truncation=True, return_tensors="pt").to(device)

# Forward pass
with torch.no_grad():
    outputs = model.generate(**inputs, max_length=512)

# Decode the output IDs to a string (SQL query in this case)
generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)

return generated_sql

input_prompt = "provide count of students where class = 10"

generated_sql = generate_sql(input_prompt)

print(f"The generated SQL query is: {generated_sql}") #expected output - SELECT COUNT(*) FROM students WHERE class = 10

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Model tree for anilajax/text2sql_industry_standard

Base model

cssupport/t5-small-awesome-text-to-sql

Finetuned

(7)

this model

anilajax
/

text2sql_industry_standard

Industry standard text to sql generation with high accuracy.

Model tree for anilajax/text2sql_industry_standard

Datasets used to train anilajax/text2sql_industry_standard