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--- |
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base_model: unsloth/DeepSeek-R1-Distill-Llama-8B |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- trl |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# About Model |
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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. |
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# DataSet |
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[b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) |
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# Model train |
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1. **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. |
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2. **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. |
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3. **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. |
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4. **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. |
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5. **train/epoch**: This chart represents the progress of each training epoch. As the global steps increase, the epoch count also steadily grows. |
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# Inference results before 和 after model training: |
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## Prompt |
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## Define SQL query for testing |
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This is a complex customer analysis query used to test the understanding of the model |
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query1 = """ |
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```sql |
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SELECT |
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pc.category_name, |
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p.product_name, |
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COUNT(DISTINCT o.customer_id) AS unique_customers, |
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COUNT(oi.order_id) AS total_sales, |
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SUM(oi.quantity) AS total_quantity_sold, |
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ROUND(AVG(oi.unit_price), 2) AS avg_selling_price, |
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SUM(oi.quantity * oi.unit_price) AS total_revenue, |
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ROUND(SUM(oi.quantity * oi.unit_price) / COUNT(DISTINCT o.customer_id), 2) AS revenue_per_customer, |
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MAX(o.order_date) AS last_sale_date, |
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MIN(o.order_date) AS first_sale_date |
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FROM product_categories pc |
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JOIN products p ON pc.category_id = p.category_id |
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JOIN order_items oi ON p.product_id = oi.product_id |
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JOIN orders o ON oi.order_id = o.order_id |
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WHERE |
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o.order_date >= '2024-01-01' |
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AND o.order_status = 'completed' |
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GROUP BY |
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pc.category_name, |
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p.product_name |
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HAVING |
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total_revenue > 10000 |
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ORDER BY |
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total_revenue DESC, |
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unique_customers DESC |
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LIMIT 15; |
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``` |
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Explain use case of this query. |
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""" |
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<think> |
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CREATE TABLE product_categories (category_name VARCHAR, product_name VARCHAR, customer_id INT, order_id INT, order_date DATETIME, status VARCHAR) |
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FROM product_categories |
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JOIN products |
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JOIN order_items |
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JOIN orders |
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WHERE order_date >= '2024-01-01' AND order_status = 'completed' |
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GROUP BY category_name, product_name |
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HAVING total_revenue > 10000 |
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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. |
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</think> |
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Below is the business use case for the query: |
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### Use Case: |
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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｜> |
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# Model Download |
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| **Model** | **Base Model** | **下载** | |
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| -------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | |
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| unsloth/DeepSeek-R1-Distill-Llama-8B | [DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B) | [🤗 HuggingFace](https://huggingface.co/jackcwf/DeepSeek-R1-Distill-Llama-4bit-sql-model/raw/main/unsloth.Q4_K_M.gguf) | |
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# Usage |
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If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. |
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# Uploaded model |
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- **Developed by:** datalabs-ai |
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- **License:** apache-2.0 |
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- **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Llama-8B |
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
