Create README.md

README.md

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+---
+license: other
+library_name: transformers
+tags:
+- language-modeling
+- transformer
+- decoder-only
+- research
+- neurips-2026-anonymous
+---
+
+# 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=16, 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.
+
+## Citation
+
+Anonymous submission under review.