You can deploy this model and start earning money today!

RichardErkhov/
aryanntated_-_SmolLM-360M-text2sql-gguf

Name: aryanntated_-_SmolLM-360M-text2sql-gguf
Availability: InStock

Views

No views yet

Quantization made by Richard Erkhov.

Github

Discord

Request more models

SmolLM-360M-text2sql - GGUF

Model creator: https://huggingface.co/aryanntated/
Original model: https://huggingface.co/aryanntated/SmolLM-360M-text2sql/

Name	Quant method	Size
SmolLM-360M-text2sql.Q2_K.gguf	Q2_K	0.2GB
SmolLM-360M-text2sql.IQ3_XS.gguf	IQ3_XS	0.2GB
SmolLM-360M-text2sql.IQ3_S.gguf	IQ3_S	0.2GB
SmolLM-360M-text2sql.Q3_K_S.gguf	Q3_K_S	0.2GB
SmolLM-360M-text2sql.IQ3_M.gguf	IQ3_M	0.21GB
SmolLM-360M-text2sql.Q3_K.gguf	Q3_K	0.22GB
SmolLM-360M-text2sql.Q3_K_M.gguf	Q3_K_M	0.22GB
SmolLM-360M-text2sql.Q3_K_L.gguf	Q3_K_L	0.23GB
SmolLM-360M-text2sql.IQ4_XS.gguf	IQ4_XS	0.21GB
SmolLM-360M-text2sql.Q4_0.gguf	Q4_0	0.21GB
SmolLM-360M-text2sql.IQ4_NL.gguf	IQ4_NL	0.21GB
SmolLM-360M-text2sql.Q4_K_S.gguf	Q4_K_S	0.24GB
SmolLM-360M-text2sql.Q4_K.gguf	Q4_K	0.25GB
SmolLM-360M-text2sql.Q4_K_M.gguf	Q4_K_M	0.25GB
SmolLM-360M-text2sql.Q4_1.gguf	Q4_1	0.23GB
SmolLM-360M-text2sql.Q5_0.gguf	Q5_0	0.25GB
SmolLM-360M-text2sql.Q5_K_S.gguf	Q5_K_S	0.26GB
SmolLM-360M-text2sql.Q5_K.gguf	Q5_K	0.27GB
SmolLM-360M-text2sql.Q5_K_M.gguf	Q5_K_M	0.27GB
SmolLM-360M-text2sql.Q5_1.gguf	Q5_1	0.27GB
SmolLM-360M-text2sql.Q6_K.gguf	Q6_K	0.34GB
SmolLM-360M-text2sql.Q8_0.gguf	Q8_0	0.36GB

Original model description:

library_name: transformers tags: []

Model Card for Model ID

Model Details

Model Description

SmolLM-360M-text2sql is a fine-tuned transformer model optimized for text-to-SQL generation tasks. The model takes natural language queries and converts them into SQL statements, enabling seamless database interactions through natural language. It was fine-tuned using the 🤗 Hugging Face Transformers library.

Developed by: Aryann Tated
Model type: Transformer-based model, fine-tuned for text-to-SQL translation

Uses

Direct Use

Convert natural language questions into SQL queries for querying databases. Build user-friendly database query systems in applications such as business intelligence dashboards and customer support tools.

Downstream Use [optional]

Integration into conversational agents for querying structured datasets.
Automating SQL generation in educational or enterprise settings.

Out-of-Scope Use

Not intended for querying highly sensitive or confidential databases without proper safeguards.
Should not be used without verifying the output SQL queries for accuracy and security.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer

Load model and tokenizer

model_name = "aryanntated/SmolLM-360M-text2sql" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name)

Example usage

input_query = "How many employees were hired in 2023?" inputs = tokenizer(input_query, return_tensors="pt") outputs = model.generate(**inputs)

Decode and print the SQL query

generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True) print(generated_sql)

Model Card Contact

Email at - aryann.k.tated@gmail.com

RichardErkhov/aryanntated_-_SmolLM-360M-text2sql-gguf