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README.md

@@ -1,22 +1,124 @@
 ---
-base_model: unsloth/llama-3-8b-bnb-4bit
+language:
+- en
 tags:
-- text-generation-inference
-- transformers
+- sql
+- text-to-sql
+- daraz
+- llama3
 - unsloth
-- llama
-- trl
+- ecommerce
 license: apache-2.0
-language:
-- en
+datasets:
+- custom
+base_model: unsloth/llama-3-8b-bnb-4bit
 ---
 
-# Uploaded  model
+# drz-sql-llama3
+
+This model is a fine-tuned version of Llama 3 (8B) for generating SQL queries specific to the Daraz e-commerce platform.
+
+## Model Description
+
+- **Base Model:** Llama 3 8B (4-bit quantized)
+- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
+- **Training Data:** 20 Daraz-specific SQL query examples
+- **Use Case:** Converting natural language questions to SQL queries for Daraz analytics
+
+## Training Details
+
+- **Framework:** Unsloth
+- **LoRA Rank:** 16
+- **Training Steps:** 100
+- **Batch Size:** 2
+- **Gradient Accumulation:** 4
+- **Learning Rate:** 0.0002
+
+## Key Features
+
+This model understands Daraz-specific:
+- Table schemas (e.g., `daraz_cdm.dwd_drz_trd_core_df`, `daraz_cdm.dwd_drz_prd_sku_extension`)
+- Business logic (Choice classification, KAM assignments, industry mapping)
+- Query patterns (MAX_PT for partitions, DATEADD for date filtering)
+- Metrics (GMV, L7/L30 calculations, order types)
+
+## Usage
+
+```python
+from unsloth import FastLanguageModel
+
+# Load model
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "Bilal326/drz-sql-llama3",
+    max_seq_length = 2048,
+    dtype = None,
+    load_in_4bit = True,
+)
+
+FastLanguageModel.for_inference(model)
+
+# Generate SQL
+alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}"""
+
+prompt = alpaca_prompt.format(
+    "Generate SQL for the following request:",
+    "Get total GMV for last 30 days in Pakistan",
+    ""
+)
+
+inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.5)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Example Queries
+
+The model can handle:
+- Simple aggregations: "Get total GMV and orders for last 30 days"
+- Complex joins: "Get seller performance with KAM assignments"
+- Time-based analysis: "Show monthly GMV trend by industry"
+- Advanced logic: "Compare Choice vs Non-Choice GMV in Crossborder"
+
+## Limitations
+
+- Trained specifically for Daraz schema and business logic
+- May not generalize to other SQL dialects or schemas
+- Requires Daraz-specific tables to be available
+
+## Training Dataset
+
+Custom dataset of 20 SQL query examples covering:
+- Revenue and GMV analysis
+- Product performance metrics
+- Seller segmentation
+- Category and brand analysis
+- Time-based trends
+
+## Citation
+
+If you use this model, please cite:
 
-- **Developed by:** Bilal326
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
+```
+@misc{drz-sql-llama3,
+  author = {Bilal326},
+  title = {drz-sql-llama3: Daraz SQL Generation Model},
+  year = {2025},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/Bilal326/drz-sql-llama3}
+}
+```
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+## Acknowledgments
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+- Built with [Unsloth](https://github.com/unslothai/unsloth)
+- Based on Meta's Llama 3
+- Fine-tuned for Daraz e-commerce analytics