Create README.md

README.md

@@ -0,0 +1,47 @@
+## 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.