RichardErkhov/itsme-nishanth_-_Sparky-SQL-Llama-3.2-1B-gguf

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Sparky-SQL-Llama-3.2-1B - GGUF

• —Model creator: https://huggingface.co/itsme-nishanth/
• —Original model: https://huggingface.co/itsme-nishanth/Sparky-SQL-Llama-3.2-1B/

Original model description: --- library_name: transformers tags:

• —unsloth
• —Sparky-SQL-Llama-3.2-1B license: apache-2.0 datasets:
• —shreeyashm/SQL-Queries-Dataset language:
• —en base_model:
• —unsloth/Llama-3.2-1B pipeline_tag: text-generation ---

Model Card for Model ID

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Model Details

Model Description

The model was fintuned from Llama-3.2-1B base model with SQL query data

How to Get Started with the Model

from transformers import pipeline
model_id = "itsme-nishanth/Sparky-SQL-Llama-3.2-1B"
pipe = pipeline("text-generation", model_id, device="cuda")
messages = [
    {"role": "user", "content": "list down the product names and its type provided by vendor 'vanhelsing' from 'products' table?"},
]
print(pipe(messages, max_new_tokens=100)[0]['generated_text'][-1])  # Print the assistant's response

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• —Developed by: Nishanth
• —Model type: Llama
• —Language(s) (NLP): English
• —License: Apache license 2.0
• —Finetuned from model : Llama-3.2-1B

Training Details

Training Data

• —SQL Dataset [https://huggingface.co/shreeyashm/SQL-Queries-Dataset]

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

Preprocessing

Dataset had empty records. Removed them before training.

Training Hyperparameters

• —Training regime:
• —gradientaccumulationsteps = 4,
• —warmup_steps = 5,
• —max_steps = 60,
• —learning_rate = 2e-4,
• —fp16 = not isbfloat16supported(),
• —bf16 = isbfloat16supported(),
• —optim = "adamw_8bit",
• —weight_decay = 0.01,
• —lrschedulertype = "linear",
• —seed = 3407

Technical Specifications

Hardware

• —Google-Colab (Tesla T4)

Software

• —Transformers
• —Unsloth

Model Card Contact

itsmenishanthkr@gmail.com