README.md

## Model Card: Max

**Model Name:** Max

**Base Model:** Gemma 3 1B IT (Instruction-Tuned, 1 billion parameters from the Gemma 3 family)

**Developed By:** IDX

**Completion Date:** May 12, 2025

**Model Description:**
Max is a language model fine-tuned from the Gemma 3 1B IT base model, specializing in code generation and comprehension, with a particular focus on the Python programming language. The model has been trained to handle code-related tasks and address technical queries, leveraging the capabilities of the state-of-the-art base model enhanced with specific knowledge acquired during the fine-tuning process on code-centric data.

**Architecture:**
The model is based on the architecture of the Gemma 3 1B model, developed by Google.

**Fine-tuning Data:**
The model was fine-tuned using curated datasets comprising:
1. Data consisting of technical questions and answers, including interactions where users describe technical challenges and others provide assistance or solutions (analogous to technical forums or Q&A platforms).
2. Examples of Python code, structured as input/output pairs.
The fine-tuning process was specifically focused on data relevant to Python code generation and technical question answering.

**Fine-tuning Process:**
The fine-tuning procedure was conducted in a Google Colab environment utilizing a single NVIDIA A100 GPU. This process adapted the Gemma 3 1B IT base model to enhance its performance on programming-related tasks and its ability to respond to code-specific inquiries.

**Intended Use Cases:**
* Generation of Python code snippets or functions based on textual descriptions.
* Answering questions regarding Python syntax, concepts, or common programming issues.
* Assisting in the explanation of Python code blocks.
* Providing support for fundamental Python programming tasks.

**Limitations:**
* Model performance is contingent upon the quality, diversity, and scope of the fine-tuning datasets.
* Primarily optimized for the Python language; performance on other programming languages may be suboptimal.
* Inherently, as a generative model, it may produce code that is incorrect, inefficient, or contains security vulnerabilities.
* Potential to inherit biases or limitations present in the base Gemma 3 model or the training data.
* The 1B version of Gemma 3 is text-only and not designed for multimodal input.
* Not suitable for deployment in critical applications without rigorous testing and human validation of generated outputs.

**Ethical Considerations:**
* Potential for generating code containing security flaws if not reviewed and validated by a human expert.
* Risk of propagating biases present in the training data (e.g., in coding styles, problem-solving approaches, etc.).
* The use of data sourced from Q&A forums implies the inclusion of user-generated content, which may contain informal language or unverified information.
* Responsible deployment and continuous human oversight of generated code and responses are strongly advised.

**Evaluation:**
Formal evaluation metrics regarding the performance of the fine-tuned model are not currently available.