Create README.md

README.md

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+---
+license: other
+library_name: transformers
+tags:
+- language-modeling
+- transformer
+- decoder-only
+- table-free-input
+- binary-token-codes
+- affine-recoding
+- research
+---
+
+# 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 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=16, 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.