---
license: apache-2.0
library_name: transformers
tags:
- causal-lm
- text-generation
- transformer
- decoder-only
- research
language:
  - en
---

# Learned Input Table Model Classic

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 **learned input table baseline**.

The model is a 32-layer decoder-only Transformer with:

- 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

This baseline uses a standard trainable input embedding table of size:

```text
65,536 x 1024 = 67,108,864 trainable input parameters
```

## Intended use

This checkpoint is provided for anonymous review and reproducibility of the paper's controlled comparison. It is intended for research use only.

## Loading example

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "E6E831728/learned-input-table-model-classic"

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 United Kingdom?\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()))
```

## Limitations

This is a small research language model trained for architectural comparison. It is not instruction-tuned for safe deployment and should not be used as a production system.

## 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.