Pandas-Tutor-Gemma-2B

A Specialized Code Assistant for the Python `pandas` Library

--- ## 🚀 Model Overview **Pandas-Tutor-Gemma-2B** is a fine-tuned version of Google's `gemma-2b-it` model, specialized for bidirectional tasks related to the Python `pandas` library. It was trained on a high-quality, curated dataset to become a reliable assistant for both novice and experienced developers. The model excels at two primary functions: 1. **Code Generation:** Translates natural language instructions into precise `pandas` code. 2. **Code Explanation:** Describes the functionality of `pandas` code snippets in clear, easy-to-understand language. This project demonstrates that with modern, parameter-efficient fine-tuning (PEFT) techniques, it's possible to create highly effective, specialized models on consumer-grade hardware. ## 🛠️ How to Use You can easily use this model with the `transformers` library. Ensure you have `transformers`, `accelerate`, and `bitsandbytes` installed. ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Model repository on Hugging Face model_name = "csmishra952/Pandas-Tutor-Gemma-2B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", torch_dtype=torch.bfloat16 ) def get_response(instruction, input_text=""): """A helper function to format the prompt and generate a response.""" prompt = f"user\n{instruction}\n{input_text}\nmodel\n" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=128) result = tokenizer.decode(outputs[0], skip_special_tokens=True) return result.split("model\n")[-1] # --- Example 1: Code Explanation (Code-to-NL) --- explanation = get_response( instruction="Explain what this Pandas code does.", input_text="df.groupby('department')['salary'].agg(['mean', 'max'])" ) print("--- Code Explanation ---") print(explanation) # --- Example 2: Code Generation (NL-to-Code) --- generated_code = get_response( instruction="Write the Pandas code to select all rows where the 'product_category' is 'Electronics' and the 'price' is less than 500." ) print("\n--- Generated Code ---") print(generated_code) ⚙️ Training DetailsModel ArchitectureThe model uses a Low-Rank Adaptation (LoRA) architecture. Instead of retraining the entire 2-billion-parameter base model, we freeze the base model and train only a small number of "adapter" matrices. This makes the training process incredibly efficient.┌───────────────────────────┐ │ │ │ Frozen Gemma-2B Model │ │ (2 Billion Parameters) │ │ │ └───────────┬───────────────┘ │ ┌───────────▼───────────────┐ │ Trainable LoRA Adapters │ │ (~0.1% of Parameters) │ └───────────────────────────┘ Fine-Tuning TechniqueThe model was fine-tuned using QLoRA, which further optimizes LoRA by loading the base model in a quantized 4-bit precision. This drastically reduces memory consumption, allowing the fine-tuning to be performed on a single T4 GPU in Google Colab.Training HyperparametersHyperparameterValueBase Modelgoogle/gemma-2b-itFine-tuning MethodQLoRALoRA r (Rank)8LoRA alpha32Precision4-bit (nf4)Compute dtypebfloat16OptimizerPaged AdamW (32-bit)Learning Rate2e-4Epochs1Batch Size1 per deviceGradient Accumulation8Training DataThe model was trained on a custom dataset of 181 high-quality examples derived from highly-voted pandas questions and their accepted answers on Stack Overflow. The data was manually cleaned, verified, and structured into a bidirectional, instruction-following format (JSONL).⚖️ LicenseThis model is licensed under the MIT License. You are free to use, modify, and distribute this model for any purpose, including commercial use.✍️ CitationIf you use this model or find this project helpful in your own work, please consider citing it:@misc{pandas_tutor_gemma_mishra, author = {Chandrasekhar Mishra}, title = {Pandas-Tutor-Gemma-2B: A Specialized Code Assistant}, year = {2025}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{[https://huggingface.co/csmishra952/Pandas-Tutor-Gemma-2B](https://huggingface.co/csmishra952/Pandas-Tutor-Gemma-2B)}} }

Pandas-Tutor-Gemma-2B

A Specialized Code Assistant for the Python pandas Library

A Specialized Code Assistant for the Python `pandas` Library

Full Model Card (README)