---
license: mit
base_model:
- bigcode/starcoder2-3b
pipeline_tag: image-to-text
---

  # 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.