soumya-006
/

CodeMentor-LLM

@@ -1,199 +1,119 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+language:
+- en
+license: apache-2.0
+tags:
+- llm
+- qlora
+- python
+- code-generation
+- instruction-tuning
+- transformers
 ---
+# CodeMentor-LLM
+CodeMentor-LLM is a lightweight coding assistant fine-tuned from Qwen2.5-1.5B-Instruct using QLoRA. The model is designed to assist with Python programming tasks, algorithm explanations, code generation, and beginner-friendly coding guidance.
 ## Model Details
+### Developed By
+Soumya Singh
+### Base Model
+Qwen/Qwen2.5-1.5B-Instruct
+### Model Type
+Causal Language Model (LLM)
+### Language
+English
+## Training Data
+The model was fine-tuned on 100 instruction-response examples from the Python Code Instructions Alpaca dataset.
+**Dataset:** `iamtarun/python_code_instructions_18k_alpaca`
+## Training Method
+- QLoRA Fine-Tuning
+- 4-bit Quantization
+- PEFT (Parameter Efficient Fine-Tuning)
+- Transformers Library
+- Hugging Face Trainer
+## Training Configuration
+| Parameter | Value |
+|------------|--------|
+| Epochs | 3 |
+| Batch Size | 2 |
+| Learning Rate | 2e-4 |
+| Gradient Accumulation | 4 |
+| Precision | FP16 |
+| GPU | NVIDIA Tesla T4 |
+## Intended Use
+This model can be used for:
+- Python code generation
+- Algorithm explanations
+- Programming tutoring
+- Beginner coding assistance
+- Educational demonstrations of LLM fine-tuning
+## Example Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model_name = "soumya-006/CodeMentor-LLM"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+prompt = """
+Instruction:
+Write a Python function to check if a number is prime.
+Response:
+"""
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=150
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## Limitations
+- Trained on only 100 examples.
+- Intended as a demonstration project.
+- May generate incorrect or inefficient code.
+- Should not be used for production systems without additional training and evaluation.
+## Future Improvements
+- Increase training dataset to 5,000+ examples.
+- Add multi-language support.
+- Improve reasoning capabilities.
+- Evaluate on standard coding benchmarks.
+- Deploy an interactive web application.
+## Author
+Soumya Singh
+B.Tech Computer Science Student
+## Hugging Face Repository
+https://huggingface.co/soumya-006/CodeMentor-LLM