File size: 2,625 Bytes
b9820e0 5350b4b b9820e0 5350b4b b9820e0 5350b4b b9820e0 cbf1b13 b9820e0 95bbbcc b9820e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | ---
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
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 **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 = "E6E831728/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 anonymous review and 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. |