ash3241
/

Amplitude-based-tokenizer

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 ---
+license: mit
+language:
+- en
+tags:
+- tokenization
+- custom-architecture
+- experimental
 ---
+# Continuous Amplitude Tokenization (CAT) - Toy Model
+This repository contains the trained weights and vocabulary for a proof-of-concept toy model utilizing the novel **Continuous Amplitude Tokenization (CAT)** architecture.
 ## Model Details
+Traditional LLMs rely on discrete BPE tokenization, forcing them to treat intensity modifiers ("very", "extremely", "slightly") as independent, arbitrary tokens. The CAT architecture splits language into two streams:
+1. **Concept ID (Discrete):** e.g., "bad", "large".
+2. **Amplitude (Continuous):** e.g., `1.0`, `2.0`, `5.0`.
+This toy model processes sentences like *"The food was incredibly bad"* as a single compressed token mapping: `[Concept: "bad", Amplitude: 4.0]`, completely eliminating the need for combinatorial adjective tokens and physically shortening sequence lengths.
+*   **Architecture:** Custom Dual-Stream Causal Transformer (CAT)
+*   **Key Components:** Fourier-FiLM Embeddings, AP-RMSNorm, 4-way Bilinear Attention, Spatio-Amplitude RoPE.
+*   **Parameters:** ~4.6 Million
+*   **Vocabulary Size:** 15,000 words (Modifiers deleted and replaced by continuous scalars)
+*   **Dataset:** 15 Million tokens (Yelp Polarity Reviews)
+*   **Training Iterations:** 1,000 (Early-stage viability test)
+## Empirical Benefits
+During benchmarking against a standard NanoGPT model on the exact same text, this CAT model achieved:
+*   **20% KV Cache Memory Savings** (due to reduced sequence lengths).
+*   **Vocab Compression** (by removing standard modifiers from the dictionary).
+*   **Zero-Shot Controllability** (ability to manually inject `Amplitude: 10.0` to force intensity scaling during generation).
+## Usage
+To use these weights, you must use the custom CAT Model code from the official GitHub repository.
+1. **Clone the architecture:**
+```bash
+git clone https://github.com/Yash943/CAT-Concept-Amplitude-Tokenization-.git
+cd CAT-Concept-Amplitude-Tokenization-
+pip install -e .
+```
+2. **Load the model (PyTorch):**
+```python
+import torch
+from cat.model import CATModel, CATConfig
+from huggingface_hub import hf_hub_download
+# Initialize the architecture
+config = CATConfig(
+    vocab_size=15000,
+    hidden_size=128,
+    num_hidden_layers=4,
+    num_attention_heads=4,
+    intermediate_size=512,
+    max_position_embeddings=64
+)
+model = CATModel(config)
+# Load the weights
+weights_path = hf_hub_download(repo_id="your-repo-id/here", filename="cat_toy_model.pt")
+model.load_state_dict(torch.load(weights_path))
+print("CAT Model loaded successfully!")
+```
+## Intended Use
+This is an experimental "toy" model designed strictly to prove the mathematical viability of Fourier-FiLM embeddings and AP-RMSNorm at reducing token fertility. It is not intended for production chat or generation tasks.
+## GitHub Repository
+For full mathematical breakdowns, empirical verification scripts, and architectural source code, visit the [official GitHub repository](https://github.com/Yash943/CAT-Concept-Amplitude-Tokenization-).