Instructions to use AquilaX-AI/NL-JSON-Start-Scan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AquilaX-AI/NL-JSON-Start-Scan with Transformers:

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

pipe = pipeline("text-generation", model="AquilaX-AI/NL-JSON-Start-Scan")

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

tokenizer = AutoTokenizer.from_pretrained("AquilaX-AI/NL-JSON-Start-Scan")
model = AutoModelForSeq2SeqLM.from_pretrained("AquilaX-AI/NL-JSON-Start-Scan", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AquilaX-AI/NL-JSON-Start-Scan with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AquilaX-AI/NL-JSON-Start-Scan

SGLang

How to use AquilaX-AI/NL-JSON-Start-Scan 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 "AquilaX-AI/NL-JSON-Start-Scan" \
    --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": "AquilaX-AI/NL-JSON-Start-Scan",
		"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 "AquilaX-AI/NL-JSON-Start-Scan" \
        --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": "AquilaX-AI/NL-JSON-Start-Scan",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AquilaX-AI/NL-JSON-Start-Scan with Docker Model Runner:
```
docker model run hf.co/AquilaX-AI/NL-JSON-Start-Scan
```

suriya7 commited on Jul 29, 2024

Commit

cd3009f

verified ·

1 Parent(s): 4ab5c71

Update README.md

Browse files

Files changed (1) hide show

README.md +34 -3

README.md CHANGED Viewed

@@ -2,12 +2,33 @@
 library_name: transformers
 license: apache-2.0
 ---
-### Requirements
 ```bash
 pip install transformers[torch]
 ```
-### Inference
 ```python
 import json
 import requests
@@ -104,4 +125,14 @@ except:
 end = time.time()
 print(to_return)
 print(f"Time taken: {end - start}")
-```

 library_name: transformers
 license: apache-2.0
 ---
+# AquilaX-NL-JSON-Start-Scan
+## Overview
+AquilaX-NL-JSON-Start-Scan is a model built using Hugging Face's T5-small to convert natural language queries about vulnerabilities into JSON queries for MongoDB.
+## Model Information
+### Model
+- **Name**: AquilaX-NL-JSON-Start-Scan
+- **Architecture**: T5-small
+- **Framework**: Hugging Face Transformers
+### Description
+The AquilaX-NL-JSON-Start-Scan model is designed to interpret natural language queries related to vulnerabilities in code and convert them into JSON queries that can be executed on a MongoDB database. This facilitates automated scanning and analysis of code repositories for security issues. The model leverages the capabilities of the T5-small architecture, which is well-suited for natural language understanding and generation tasks.
+# Getting Started
+## Usage
+Below we share some code snippets on how to get quickly started with running the model. First make sure to pip install -U transformers[torch], then copy the snippet from the section.
+## Requirements
 ```bash
 pip install transformers[torch]
 ```
+## Inference Code
 ```python
 import json
 import requests
 end = time.time()
 print(to_return)
 print(f"Time taken: {end - start}")
+```
+## License
+This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
+## Authors
+- [Aquilax-Ai](https://huggingface.co/AquilaX-AI)
+## Acknowledgments
+- Hugging Face for the Transformers library.
+- Any other acknowledgments.