Instructions to use Majipa/text-to-SQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Majipa/text-to-SQL with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Majipa/text-to-SQL", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Majipa/text-to-SQL with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Majipa/text-to-SQL to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Majipa/text-to-SQL to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Majipa/text-to-SQL to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Majipa/text-to-SQL", max_seq_length=2048, )
Uploaded model
- Developed by: Majipa
- License: apache-2.0
- Finetuned from model : unsloth/phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
Using the model
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
model = AutoModelForCausalLM.from_pretrained("Majipa/text-to-SQL",
device_map="cuda",
torch_dtype="auto",
quantization_config=quantization_config)
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
messages = [
{"role": "system", "content": "You are a helpful text-to-SQL assistant."},
{"role": "user", "content": "question: How many heads of the departments are older than 56 ? context: CREATE TABLE head (age INTEGER)"},
]
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 500,
"temperature": 0.7,
}
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
Inference Providers NEW
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