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license: apache-2.0
library_name: transformers
tags:
- causal-lm
- text-generation
- transformer
- decoder-only
- table-free-input
- binary-token-codes
- affine-recoding
- research
language:
- en
---
# Affine-Recoded Minimal Code Table-Free Model
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 **fully table-free affine-recoded minimal binary-code model**.
The model does not use an input embedding table. Instead, token codes are computed directly from token IDs.
For each token ID `t`, the model computes:
```text
c(t) = bin_16(t)
```
and then applies a fixed invertible affine recoding over GF(2):
```text
c_tilde(t) = A c(t) xor b
```
where:
- `A` is an invertible binary matrix in `GL(16, 2)`
- `b` is a fixed binary shift vector
The resulting 16-dimensional binary code is tiled to model width 1024.
The model uses:
```text
0 trainable input-embedding parameters
0 input embedding table
```
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 = "Bochkov/llm-fix-min-affine-recoded-minimal-code-table-free"
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 UK?\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 reproducibility. It demonstrates that the fixed minimal-code input interface remains viable even when the canonical token-ID binary code is randomly recoded by an invertible affine transform.
## 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.
---
## 🧑🔬 Citation & Concept
If you use this model or the underlying concepts in your research, please cite our work:
```
@misc{bochkov2026languagemodelstrainableinput,
title={Language Models Without a Trainable Input Embedding Table: Learning from Fixed Minimal Binary Token Codes},
author={A. Bochkov},
year={2026},
eprint={2605.09751},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2605.09751},
}
``` |