---
license: apache-2.0
tags:
  - text-generation
  - causal-lm
  - transformer
  - research
  - interpretability
  - multilingual
  - unicode
  - frozen-embeddings
  - ablation
language:
  - multilingual
library_name: transformers
pipeline_tag: text-generation
---

# Emergent Semantics — Model_256_FLOAT (285M)

This repository provides **Model_256_FLOAT (285M)** — an **ablation model** from the paper:

[📚 Paper (Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations)](https://huggingface.co/papers/2507.04886) -

[📚 Paper (Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate)](https://huggingface.co/papers/2507.07129) -

[📚 Blog Article](https://huggingface.co/blog/Bochkov/emergent-semantics-beyond-token-embeddings)

This checkpoint isolates the effect of **floating-point / normalized frozen embeddings** (and the geometry they induce), while still keeping the embeddings **non-trainable** and **non-semantic**.

---

## Key idea (what this ablation tests)

This model is a close counterpart to **Model_256_BIT**, but the embedding vectors are **floats** rather than **binary**.

Pipeline (high-level):

1. Assign each token a **random unique code** (collision-free “unique ID per token” guaranteed by construction).
2. Convert the code into a vector representation.
3. Apply **PCA projection** to obtain a compact `n_embed = 256` representation.
4. Apply **L2 normalization** (so each token embedding has unit norm).
5. Freeze the embedding table (`requires_grad=False`) during training.

So **Model_256_FLOAT** tests whether improvements/convergence differences come from:
- simply having a stable token identifier (random, frozen), **or**
- additionally having a *continuous normalized geometry* (float values + normalization), even without any semantic or glyph information.

To match the Transformer hidden size, the 256-dim embedding is expanded to 1024 via a **non-trainable repetition**:
`repeat_interleave(4)` → `256 * 4 = 1024`.

---

## Important: parameter count difference (vs 335M models)

This checkpoint has **~285M parameters**, while models with a standard `n_embed=1024` embedding table (e.g. **UNI_GLYPH / unfrozen baselines**) are **~335M**.

The difference is primarily the embedding table size:

- Standard embedding params: `vocab_size * 1024 = 65536 * 1024 ≈ 67.1M`
- This model’s embedding params: `vocab_size * 256 = 65536 * 256 ≈ 16.8M`

The Transformer backbone is the same (layers/heads/d_model), but the total parameter count is lower because the embedding matrix is smaller.

---

## Model summary

- **Architecture:** decoder-only Transformer (GPT-like)
- **Hidden size (`d_model`):** 1024  
- **Layers:** 16  
- **Heads:** 32  
- **Positional encoding:** rotary embeddings  
- **Activation:** GELU  
- **Tokenizer / vocab size:** 65,536 (bvv241-2-3 compatible)
- **Input embeddings:** **frozen**, float, `n_embed=256`, derived from random unique IDs + **PCA + L2 normalization**, expanded to 1024 by repetition (non-trainable)
- **Output head:** **not tied** to the input embeddings (trained separately)

---

## Tokenizer

The intended tokenizer is **bvv241-2-3** (same vocab size and indexing):

- https://huggingface.co/Bochkov/bvv241-2-3

You may load the tokenizer either from this model repo (if included) or from the standalone tokenizer repo. The key requirement is **exact vocab alignment**.

---

## How to use (Transformers)

```python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bochkov/emergent-semantics-model-256-float-285m")
model = AutoModelForCausalLM.from_pretrained("Bochkov/emergent-semantics-model-256-float-285m", trust_remote_code=True).to('cuda')

inputs = torch.tensor([tokenizer.encode("Question: What is the capital of Japan?\nAnswer:")], dtype=torch.long, device='cuda')

outputs = model.generate(
    inputs, 
    max_new_tokens=10,
    do_sample=False
)
print(tokenizer.decode(outputs[0].tolist()))

#Question: What is the capital of Japan?
#Answer:San Juan

```

---

## Intended use

This model is intended for **research only**, especially for:

- Comparing **binary vs float normalized** frozen embeddings under the same `n_embed`
- Studying whether **normalization / continuous geometry** affects convergence and reasoning benchmarks
- Controlled comparisons vs:
  - **Model_256_BIT**
  - **Model_UNI_GLYPH**
  - trainable-embedding baselines

Not intended for production deployment.

---

## Related links

- **Model collection (paper artifacts):**  
  https://huggingface.co/collections/Bochkov/emergent-semantics-beyond-token-embeddings
- **UNI_GLYPH main model (frozen visual glyph embeddings):**  
  https://huggingface.co/Bochkov/emergent-semantics-model-uni-glyph-335m
- **Tokenizer:**  
  https://huggingface.co/Bochkov/bvv241-2-3
- **Code (GitHub):**  
  https://github.com/AVBochkov/Embeddings

---

## 🧑‍🔬 Citation & Concept
If you use this model or the underlying concepts in your research, please cite our work:
```
@article{
      bochkov2025emergent,
      title={Emergent Semantics Beyond Token Embeddings: Transformer {LM}s with Frozen Visual Unicode Representations},
      author={Andrey Bochkov},
      journal={Transactions on Machine Learning Research},
      issn={2835-8856},
      year={2025},
      url={https://openreview.net/forum?id=Odh8IynO1o},
      note={}
}
@misc{bochkov2025growingtransformersmodularcomposition,
      title={Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate}, 
      author={A. Bochkov},
      year={2025},
      eprint={2507.07129},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2507.07129}, 
}
```