Create README.md

README.md

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+---
+license: mit
+base_model:
+- bigcode/starcoder2-3b
+pipeline_tag: image-to-text
+---
+---
+title: "Gesture-to-Code Adapter for StarCoder2-3B"
+license: "MIT"
+language: "en"
+tags:
+  - sign-language
+  - code
+  - code-generation
+  - accessibility
+datasets:
+  - your-gesture-dataset
+metrics:
+  - cosine-similarity
+  - accuracy
+widget:
+  - example_input: "Gesture embedding: [some vector or placeholder data]"
+    example_output: "Generated code snippet"
+
+# Below is the full model card description in YAML multiline format.
+# Adjust headings and text as appropriate for your project.
+model_card: |
+  # Gesture-to-Code Adapter for StarCoder2-3B
+
+  ## Model Description
+  This repository contains a **Gesture-to-Code Adapter** designed to work with the **StarCoder2-3B** language model. By injecting gesture embeddings into the StarCoder2-3B token space, the adapter enables real-time translation of recognized gestures into structured programming code. It leverages StarCoder2-3B’s powerful code generation capabilities, extending them to multimodal input.
+
+  ### Key Features
+  - **Base Model**: [StarCoder2-3B](https://huggingface.co/), a 3-billion parameter LLM specialized in code.
+  - **Adapter**: A lightweight MLP-based projection layer that aligns gesture embeddings (from a CNN or other visual encoder) to StarCoder2-3B’s 3072-dim token embeddings.
+  - **Training Objective**: Mean-squared error (MSE) alignment of gesture–token pairs, plus optional contrastive alignment to refine embeddings.
+  - **Usage**: Real-time sign language to code snippet generation, focusing on accessibility for Deaf or hard-of-hearing programmers.
+
+  ## Dataset
+  - **Name**: A custom gesture dataset containing images for typical code-related gestures (e.g., “for loop,” “if statement,” “function definition”).
+  - **Format**: Each gesture is an image or short video snippet, which is converted to a fixed-size CNN embedding. The embedding is labeled to match the intended code structure.
+  - **Scale**: The dataset includes around XX,000 samples, covering ~XX discrete gestural instructions.
+
+  ## Training Process
+  1. **Gesture Encoder**: A CNN-based classifier extracts 256- or 512-dimensional embeddings from sign images.
+  2. **Adapter Learning**: We train a simple projection (fully connected + activation) to map these embeddings into StarCoder2-3B’s input space.  
+  3. **Integration**: During code generation, the adapter’s output replaces a special token’s embedding (e.g., `<G>`). The code model then produces a relevant code snippet conditioned on the recognized gesture.
+
+  ## Model Performance
+  - **Cosine Similarity** between the adapter’s outputs and the matched StarCoder2-3B tokens.  
+  - **Accuracy/F1** on sign-to-code classification for recognized gestures.  
+  - **Code Quality**: Preliminary tests show valid syntax ~XX% of the time, with advanced logic requiring additional prompt context or manual checks.
+
+  ## Intended Use
+  1. **Accessibility**: Provide a new input modality for coding, especially beneficial for Deaf/hard-of-hearing individuals.
+  2. **Educational Tools**: Enable sign-based code demonstrations in academic settings or coding bootcamps.
+  3. **Research**: Investigate multimodal alignment between visual gestures and textual code embeddings.
+
+  ## Limitations
+  - **Limited Gesture Set**: Only covers a subset of sign language gestures and code constructs. Expanding coverage requires additional labeled data.
+  - **Hardware Requirements**: Real-time inference typically requires GPU acceleration for both CNN and StarCoder2-3B.  
+  - **Complex Code**: While StarCoder2-3B is advanced, complicated multi-file or large project code generation might not be end-to-end feasible.
+
+  ## How to Use
+  ```python
+  from transformers import AutoModel
+
+  # 1. Load StarCoder2-3B
+  starcoder = AutoModel.from_pretrained("starcoder2-3b")
+
+  # 2. Load the adapter
+  # e.g., adapter = load_adapter("YourName/gesture2code_adapter")
+
+  # 3. Integration snippet
+  # For a recognized gesture -> CNN embedding -> adapter -> StarCoder2-3B token
+  # Replace special token <G> embedding with adapter output.