Instructions to use thekraftors/text-to-sql-beta-v1-q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thekraftors/text-to-sql-beta-v1-q8 with Transformers:

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

pipe = pipeline("text-generation", model="thekraftors/text-to-sql-beta-v1-q8")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("thekraftors/text-to-sql-beta-v1-q8", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use thekraftors/text-to-sql-beta-v1-q8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thekraftors/text-to-sql-beta-v1-q8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thekraftors/text-to-sql-beta-v1-q8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/thekraftors/text-to-sql-beta-v1-q8

SGLang

How to use thekraftors/text-to-sql-beta-v1-q8 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 "thekraftors/text-to-sql-beta-v1-q8" \
    --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": "thekraftors/text-to-sql-beta-v1-q8",
		"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 "thekraftors/text-to-sql-beta-v1-q8" \
        --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": "thekraftors/text-to-sql-beta-v1-q8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use thekraftors/text-to-sql-beta-v1-q8 with Docker Model Runner:
```
docker model run hf.co/thekraftors/text-to-sql-beta-v1-q8
```

text-to-sql-beta-v1-q8 / README.md

thekraftors

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	---
	license: apache-2.0
	language:
	- en
	---
	# Your Project Name

	## Description
	Briefly describe your project and its purpose.

	## Files
	- `.gitattributes`: Git configuration file.
	- `README.md`: This file, containing information about the project.
	- `initial commit`: Description of the initial commit.
	- `checkpoint`: Checkpoint file.
	- `counter`: File related to counting.
	- `encoder.json`: JSON file for the encoder.
	- `events.out.tfevents.1705309987.5f53e38a8413`: TensorFlow events file.
	- `hparams.json`: JSON file containing hyperparameters.
	- `model-5000.data-00000-of-00001`: Model data file.
	- `model-5000.index`: Model index file.
	- `model-5000.meta`: Model meta file.
	- `vocab.bpe`: Vocabulary file.

	## Usage
	Explain how to use or run your project. Include any dependencies and setup instructions.

	## Model Training
	If applicable, provide information on how the model was fine-tuned and any specific training steps.

	## License
	Specify the license under which your project is released.

	## Acknowledgments
	Give credit to any resources, libraries, or individuals you used or were inspired by during the development.

	## Contact
	Provide contact information for the maintainer or contributors, in case someone wants to reach out.

	Feel free to customize this template based on the specific details of your project.