Update README.md

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library_name: transformers
tags: []
---

# Model Card for Model ID

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## Model Details

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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## Uses

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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## Training Details

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#### Summary



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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Glossary [optional]

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README.md

@@ -1,50 +0,0 @@
-
-
-
-# **Sql_LLama2_V3 - Text-to-SQL Model**  
-_A fine-tuned LLaMA 2 model for converting natural language queries into SQL._
-
-## **Model Details**  
-- **Model Name**: `Sql_LLama2_V3`  
-- **Repository**: `khalifa1/Sql_LLama2_V3`  
-- **Base Model**: LLaMA 2  7B
-- **Training Dataset**: Fine-tuned on Text-to-SQL datasets  Spider/WikiSQL  
-- **Framework**: Hugging Face Transformers (`AutoModelForCausalLM`)  
-- **Use Case**: SQL Query Generation from Natural Language  
-- **Model Type**: Causal Language Model (CLM)  
-- **Hardware Used for Training**: (Specify GPU type, e.g., A100, 3090, etc.)  
-
-## **Model Description**  
-`Sql_LLama2_V3` is a fine-tuned version of LLaMA 2 designed for **Text-to-SQL** tasks. It translates user questions into accurate SQL queries while understanding **database schemas, joins, and conditions**. The model has been optimized for **business intelligence, database management, and CRM applications**.
-
-## **Installation & Usage**  
-### **Load the Model in Python**
-```python
-model_name = "khalifa1/Sql_LLama2_V3"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-# if you have atleast 15GB of GPU memory, run load the model in float16
-model = AutoModelForCausalLM.from_pretrained(
-        model_name,
-        trust_remote_code=True,
-        torch_dtype=torch.float16,
-        device_map="auto",
-        use_cache=True,
-    )
-
-```
-## **Use Cases**
-✅ **Automated SQL Query Generation**  
-✅ **Database Chatbots & Assistants**  
-✅ **CRM & Enterprise Data Retrieval**  
-✅ **Business Intelligence & Reporting**  
-
-## **Model Performance**
-- Evaluated on **Text-to-SQL benchmarks** (Spider)
-- Supports **complex queries with JOINs, WHERE conditions, and aggregations**  
-
-
-## **Limitations**
- **May require fine-tuning** for specific database schemas  
- **Not optimized for large-scale databases without retrieval-augmented generation (RAG)**  
-
----