datalabs-ai/DeepSeek-R1-Distill-Llama-1.5B-sqltotxt-model
About Model
Fine-tuning is used to convert SQL language into natural language, making it easier for users to understand the business meaning of SQL queries. This fine-tuned model is based on the unsloth framework AND uses the DeepSeek-R1-Distill-Llama-8B pre-trained model under unsloth.
DataSet
Model train
- train/loss: This chart shows the model's loss during training. As the training steps (global step) increase, the loss value drops sharply 和 then stabilizes, indicating that the model is gradually converging.
- train/learning_rate: This chart shows how the learning rate changes over training steps. From the chart, we can see that the learning rate decreases as training progresses, which is likely part of a learning rate decay strategy to prevent the model from oscillating in the later stages of training.
- train/grad_norm: This chart displays the change in gradient norm over training steps. The decrease in gradient norm suggests that the gradients are stabilizing, reducing instability during training.
- train/global_step: This chart shows the increase in global training steps. As the training progresses, the step count gradually increases, indicating the progress of the training process.
- train/epoch: This chart represents the progress of each training epoch. As the global steps increase, the epoch count also steadily grows.
Inference results before 和 after model training:
Prompt
Define SQL query for testing
This is a complex customer analysis query used to test the understanding of the model
query1 = """
SELECT
pc.category_name,
p.product_name,
COUNT(DISTINCT o.customer_id) AS unique_customers,
COUNT(oi.order_id) AS total_sales,
SUM(oi.quantity) AS total_quantity_sold,
ROUND(AVG(oi.unit_price), 2) AS avg_selling_price,
SUM(oi.quantity * oi.unit_price) AS total_revenue,
ROUND(SUM(oi.quantity * oi.unit_price) / COUNT(DISTINCT o.customer_id), 2) AS revenue_per_customer,
MAX(o.order_date) AS last_sale_date,
MIN(o.order_date) AS first_sale_date
FROM product_categories pc
JOIN products p ON pc.category_id = p.category_id
JOIN order_items oi ON p.product_id = oi.product_id
JOIN orders o ON oi.order_id = o.order_id
WHERE
o.order_date >= '2024-01-01'
AND o.order_status = 'completed'
GROUP BY
pc.category_name,
p.product_name
HAVING
total_revenue > 10000
ORDER BY
total_revenue DESC,
unique_customers DESC
LIMIT 15;Explain use case of this query. """
<think> CREATE TABLE productcategories (categoryname VARCHAR, productname VARCHAR, customerid INT, orderid INT, orderdate DATETIME, status VARCHAR) FROM productcategories JOIN products JOIN orderitems JOIN orders WHERE orderdate >= '2024-01-01' AND orderstatus = 'completed' GROUP BY categoryname, productname HAVING total_revenue > 10000
This query analyzes the relationship between product categories and customer orders to identify categories and products with the highest revenue in completed orders since 2024. It can help a business understand which products are generating significant revenue and which products are performing well in the category to inform inventory planning and pricing strategies. </think> Below is the business use case for the query:
Use Case:
This query analyzes the relationship between product categories and customer orders to identify categories and products with the highest revenue in completed orders since 2024. It can help a business understand which products are generating significant revenue in the category and which products are performing well in the category to inform inventory planning and pricing strategies.<｜end▁of▁sentence｜>
Model Download
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
Uploaded model
- Developed by: datalabs-ai
- License: apache-2.0
- Finetuned from model : unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.