Instructions to use Mrigendra100/XpathGenerator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mrigendra100/XpathGenerator with Transformers:

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

pipe = pipeline("text-generation", model="Mrigendra100/XpathGenerator")

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

tokenizer = AutoTokenizer.from_pretrained("Mrigendra100/XpathGenerator")
model = AutoModelForCausalLM.from_pretrained("Mrigendra100/XpathGenerator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mrigendra100/XpathGenerator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mrigendra100/XpathGenerator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mrigendra100/XpathGenerator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mrigendra100/XpathGenerator

SGLang

How to use Mrigendra100/XpathGenerator 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 "Mrigendra100/XpathGenerator" \
    --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": "Mrigendra100/XpathGenerator",
		"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 "Mrigendra100/XpathGenerator" \
        --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": "Mrigendra100/XpathGenerator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mrigendra100/XpathGenerator with Docker Model Runner:
```
docker model run hf.co/Mrigendra100/XpathGenerator
```

XpathGenerator / README.md

Mrigendra100

Update README.md

cd0d6e0 verified about 2 years ago

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---
language:
- en
base_model: EleutherAI/gpt-neo-1.3B
pipeline_tag: text-generation
library_name: transformers
---
# Model Card for Mrigendra100/XpathGenerator

## Model Details
- **Model Name**: Mrigendra100/XpathGenerator
- **Base Model**: EleutherAI/gpt-neo-1.3B
- **Task**: Text generation (specifically for generating XPath expressions from HTML)

## Library
- **Transformers**: This model uses the Hugging Face `transformers` library for handling pre-trained models and performing inference.

## Usage
To use this model, you can leverage the `transformers` library from Hugging Face. Below is an example of how to load and use the model for inference:

```python
from transformers import pipeline
# Load the model and tokenizer
generator = pipeline("text-generation", model="Mrigendra100/XpathGenerator")
# Generate XPath from input HTML
input_html = '<button class="btn relative btn-primary shrink-0"><div class="flex justify-center">Get Plus</div></button>'
generated_xpath = generator(input_html, max_length=50)
print("Generated XPath:", generated_xpath)

	---
	language:
	- en
	base_model: EleutherAI/gpt-neo-1.3B
	pipeline_tag: text-generation
	library_name: transformers
	---
	# Model Card for Mrigendra100/XpathGenerator

	## Model Details
	- Model Name: Mrigendra100/XpathGenerator
	- Base Model: EleutherAI/gpt-neo-1.3B
	- Task: Text generation (specifically for generating XPath expressions from HTML)

	## Library
	- Transformers: This model uses the Hugging Face `transformers` library for handling pre-trained models and performing inference.

	## Usage
	To use this model, you can leverage the `transformers` library from Hugging Face. Below is an example of how to load and use the model for inference:

	```python
	from transformers import pipeline

	# Load the model and tokenizer
	generator = pipeline("text-generation", model="Mrigendra100/XpathGenerator")

	# Generate XPath from input HTML
	input_html = '<button class="btn relative btn-primary shrink-0"><div class="flex justify-center">Get Plus</div></button>'
	generated_xpath = generator(input_html, max_length=50)

	print("Generated XPath:", generated_xpath)