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license: other
library_name: transformers
tags:
- language-modeling
- transformer
- decoder-only
- fixed-embeddings
- binary-token-codes
- research
---
# 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:
```text
V = 65,536
```
the minimal injective binary code width is:
```text
K = ceil(log2(V)) = 16
```
The 16-dimensional binary code is tiled to model width 1024.
The model therefore uses:
```text
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
```python
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. |