Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+  - text-to-sql
+  - generative-ai
+  - lora
+  - qwen
 ---
 
-# Model Card for Model ID
+# Model Card for Qwen2.5-0.5B Text-to-SQL
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Summary
 
+This model converts **natural language questions into SQL queries**.  
+It is a fine-tuned version of **Qwen2.5-0.5B**, adapted specifically for the **Text-to-SQL** task using the **LoRA (Low-Rank Adaptation)** method.
 
+The model is designed to be lightweight, efficient, and suitable for local experimentation and educational purposes.
+
+---
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** Melih Emin  
+- **Model type:** Causal Language Model (Text-to-SQL)  
+- **Language(s):** English  
+- **License:** Apache 2.0  
+- **Finetuned from model:** Qwen/Qwen2.5-0.5B  
+- **Fine-tuning method:** LoRA (Low-Rank Adaptation)
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+This model was fine-tuned as part of a **Generative Artificial Intelligence course assignment**.  
+The primary goal was to explore parameter-efficient fine-tuning techniques on limited local hardware.
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
+- **Base Model:** https://huggingface.co/Qwen/Qwen2.5-0.5B  
+- **Repository:** https://huggingface.co/melihemin/qwen2.5-0.5b-text2sql-full  
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+---
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+- Converting English questions into SQL queries
+- Educational demonstrations of Text-to-SQL systems
+- Local experimentation with small language models
 
-[More Information Needed]
+### Downstream Use
 
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+- Can be integrated into database query assistants
+- Can serve as a baseline for more advanced Text-to-SQL systems
+- Further fine-tuning with schema-specific datasets
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- Production-grade database querying without validation
+- Complex multi-database or highly nested SQL queries
+- Security-critical or sensitive data environments
 
-[More Information Needed]
+---
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- The model may generate **syntactically valid but semantically incorrect SQL**
+- It does not perform schema validation
+- Performance depends heavily on prompt structure
+- Trained on a limited dataset and may not generalize to unseen schemas
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- Always validate generated SQL before execution
+- Use schema-aware prompting for better results
+- Do not use directly in production without safeguards
 
-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
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### 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 [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-## More Information [optional]
+model_id = "melihemin/qwen2.5-0.5b-text2sql-full"
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
 
-## Model Card Authors [optional]
+prompt = """### Question:
+How many heads of the departments are older than 56?
 
-[More Information Needed]
+### SQL:
+"""
 
-## Model Card Contact
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=128)
 
-[More Information Needed]
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))