E6E831728/fixed-minimal-binary-code

Fixed Minimal Binary Code Model

This is an anonymized research checkpoint for the paper:

Language Models Without a Trainable Input Embedding Table: Learning from Fixed Minimal Binary Token Codes

Model variant

This repository contains the fixed minimal binary token-code model.

Instead of a trainable input embedding table, each token ID is represented by its exact minimal binary code.

For vocabulary size:

V = 65,536

the minimal injective binary code width is:

K = ceil(log2(V)) = 16

The 16-dimensional binary code is tiled to model width 1024.

The model therefore uses:

0 trainable input-embedding parameters

The output projection remains standard and trainable.

Architecture

• decoder-only Transformer
• vocabulary size: 65,536
• model width: 1024
• number of layers: 32
• number of attention heads: 32
• context length: 1024
• rotary positional embeddings
• GELU activations
• untied trainable output projection

Loading example

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "E6E831728/fixed-minimal-binary-code"

tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)
model.eval()

prompt = "Question: What is the capital of France?\nAnswer:"
input_ids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long)

with torch.no_grad():
    output_ids = model.generate(input_ids, max_new_tokens=3, do_sample=False)

print(tokenizer.decode(output_ids[0].tolist()))

Intended use

This checkpoint is provided for anonymous review and reproducibility of the paper's main claim: a trainable input embedding table is not necessary for useful language modeling in the studied regime.

Limitations

This model is a research checkpoint. It is not intended for deployment. It may produce incorrect, biased, unsafe, or nonsensical outputs.

Training data

The model was trained on the same FineWeb-Edu + Cosmopedia mixture used for the matched comparisons in the paper. Dataset terms and licenses are those of the original datasets.