Create README.md

README.md

@@ -0,0 +1,55 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+tags:
+  - code-review
+  - python
+  - qwen2
+  - fine-tuned
+datasets:
+  - custom
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# Code Review Critic
+
+A fine-tuned Qwen2.5-Coder-7B-Instruct model for Python code review.
+
+## Model Description
+
+This model provides constructive, actionable feedback on Python code. It focuses on:
+- Bug detection
+- Potential issues
+- Code quality improvements
+
+**Base Model:** Qwen/Qwen2.5-Coder-7B-Instruct
+**Fine-tuning Method:** QLoRA (4-bit quantization + LoRA adapters)
+**Training Data:** 8,275 real GitHub PR review comments from major Python projects
+
+## Training Details
+
+- **LoRA Rank:** 64
+- **LoRA Alpha:** 64
+- **Learning Rate:** 2e-4
+- **Epochs:** 2
+- **Final Eval Loss:** 0.8455
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("YOUR_USERNAME/code-review-critic")
+tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/code-review-critic")
+
+messages = [
+    {"role": "system", "content": "You are an expert code reviewer. Analyze the provided Python code and give constructive, specific feedback."},
+    {"role": "user", "content": "Review this Python code:\n\n```python\ndef get_user(id):\n    return db.query(f'SELECT * FROM users WHERE id = {id}')\n```"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))