Update README.md

README.md

@@ -1,199 +1,134 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+tags:
+- code-review
+- security-analysis
+- static-analysis
+- python
+- code-quality
+- peft
+- qlora
+- fine-tuned
+- sql-injection
+- vulnerability-detection
+- python-security
+- code-optimization
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
+# Code Review Assistant Model
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
+A specialized Python code review assistant fine-tuned for security analysis, performance optimization, and Pythonic code quality. The model identifies security vulnerabilities, performance issues, and provides corrected code examples with detailed explanations specifically for Python codebases.
 
 ## 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:** [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 is a fine-tuned version of Qwen2.5-7B-Instruct, specifically optimized for Python code analysis. It excels at detecting security vulnerabilities, performance bottlenecks, and code quality issues while providing actionable fixes with corrected code examples.
 
-### Model Sources [optional]
+- **Developed by:** Alen Philip
+- **Model type:** Causal Language Model
+- **Language(s) (NLP):** English, with specialized Python code understanding
+- **License:** Apache 2.0
+- **Finetuned from model:** Qwen/Qwen2.5-7B-Instruct
+- **Supported Languages:** Python only
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Hugging Face Hub](https://huggingface.co/alenphilip/Code_Review_Assistant_Model)
+- **Base Model:** [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
+- **Training Dataset:** [Code Review Dataset](https://huggingface.co/datasets/alenphilip/Code-Review-Assistant)
 
 ## 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. -->
-
-[More Information Needed]
+This model is specifically designed for:
+- Automated Python code review in development pipelines
+- Security vulnerability detection in Python code
+- Python code quality assessment and improvement suggestions
+- Performance optimization recommendations for Python applications
+- Educational purposes for learning Python best practices
+- Integration into Python IDEs and code editors
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be integrated into:
+- CI/CD pipelines for automated Python code review
+- Python code quality monitoring tools
+- Security scanning platforms for Python applications
+- Educational platforms for Python programming
+- Code review assistance tools for Python developers
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- Analysis of non-Python programming languages
+- Non-code related text generation
+- Legal or compliance advice
+- Production deployment without human validation
+- Real-time security monitoring without additional safeguards
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- **Language Specificity:** Only trained on Python code - will not perform well on other programming languages
+- **False Positives/Negatives:** May occasionally miss edge cases or flag non-issues
+- **Training Data Bias:** Reflects patterns and conventions present in the training dataset
+- **Security Critical Systems:** Should not be sole security measure for critical systems
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users should:
+- Always validate model suggestions with human review
+- Use as assistant tool rather than autonomous system
+- Test suggested fixes thoroughly before deployment
+- Combine with other security scanning tools for critical applications
 
 ## 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]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_name = "alenphilip/Code_Review_Assistant_Model"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Example usage for code review
+def review_python_code(code_snippet):
+    messages = [
+        {"role": "system", "content": "You are a helpful AI assistant specialized in code review and security analysis."},
+        {"role": "user", "content": f"Review this Python code and provide improvements with fixed code:\n\n```python\n{code_snippet}\n```"}
+    ]
+    
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=False
+    )
+    
+    inputs = tokenizer(text, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return response
+
+# Test with vulnerable code
+vulnerable_code = '''
+def get_user_by_email(email):
+    query = "SELECT * FROM users WHERE email = '" + email + "'"
+    cursor.execute(query)
+    return cursor.fetchone()
+'''
+
+result = review_python_code(vulnerable_code)
+print(result)
 
-## Model Card Contact
 
-[More Information Needed]