Emergent Semantics — Model_1024_BIT (335M)

This repository provides Model_1024_BIT (335M) — an ablation model from the paper:

📚 Paper (Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations) -

📚 Paper (Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate) -

This checkpoint is designed to test whether Transformer LMs can learn strong internal semantic structure even when the input embedding space is frozen, non-semantic, and random.

What this ablation is

Model_1024_BIT uses a frozen embedding table where:

n_embed = 1024 (embedding dimensionality equals d_model)
Each token embedding is a binary vector (0/1 components)
Vectors are randomly generated, but we guarantee a unique ID per token (no collisions) by construction
The embedding weights are frozen (requires_grad=False) for the entire training run

This makes the embedding layer a fixed “token identifier substrate” rather than a learned semantic space.

Relation to other models in the collection

Compared to Model_UNI_GLYPH (335M):
- Same Transformer backbone (d_model=1024, 16 layers, 32 heads, RoPE, GELU)
- Same embedding dimensionality (n_embed=1024)
- Difference is only the embedding initialization:
  - UNI_GLYPH: deterministic glyph bitmap → PCA → normalization
  - 1024_BIT: random binary vectors, unique per token, frozen
Compared to Model_unfrozen (335M):
- Same architecture
- Here embeddings are frozen and random; in the baseline they are trainable

Because n_embed=1024, this model is in the same parameter-count class (~335M) as UNI_GLYPH and the unfrozen baseline.

Model summary

Architecture: decoder-only Transformer (GPT-like)
Hidden size (d_model): 1024
Layers: 16
Heads: 32
Positional encoding: rotary embeddings
Activation: GELU
Vocabulary size: 65,536
Tokenizer: Bochkov/bvv241-2-3 compatible
Input embeddings: frozen, random binary, n_embed=1024 (no collisions)
Output head: not tied to the input embeddings (trained separately)

Files in this repo (embedding reference)

This repo includes the frozen embedding content used by the model (for reproducibility/inspection).

If you also publish a plaintext dump similar to the 16-bit model (recommended), add a link here, e.g.:

embeddings.txt (reference mapping token → 1024-bit vector):
https://huggingface.co/Bochkov/emergent-semantics-model-1024-bit-335m/blob/main/embeddings.txt

Note: Keeping the frozen embedding tensor (and optionally a readable dump) inside the model repo makes the checkpoint self-contained and avoids ambiguity if someone loads the tokenizer from elsewhere.

Tokenizer

The intended tokenizer is bvv241-2-3:

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

You can 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)


import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bochkov/emergent-semantics-model-1024-bit-335m")
model = AutoModelForCausalLM.from_pretrained("Bochkov/emergent-semantics-model-1024-bit-335m", 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:Takayama

Verify the 1024-bit frozen binary embeddings (sanity check)

The model uses a frozen nn.Embedding(vocab_size=65536, n_embed=1024) whose values are strictly binary (0/1).

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "Bochkov/emergent-semantics-model-1024-bit-335m"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)
model.eval()

print("vocab_size:", tokenizer.vocab_size)
print("config:", {k: getattr(model.config, k) for k in ["vocab_size", "n_embed", "d_model", "n_layer", "n_head", "scale"]})

# --- 1) Show embedding matrix shape (should be 65536 x 1024) ---
W = model.token_embeddings.weight.detach().cpu()
print("token_embeddings.weight shape:", tuple(W.shape))  # (65536, 1024)

# --- 2) Tokenize 'A' and show its token id  ---
text = "A"
ids = tokenizer.encode(text, add_special_tokens=False)
tokens = tokenizer.convert_ids_to_tokens(ids)

print(f"text={text!r}")
print("ids:", ids)
print("tokens:", tokens)

tid = ids[0]

# --- 3) Print the 256 dim vector and verify it is binary (0/1) ---
e1024= W[tid]  # shape: (1024)
print("1024-dim embedding for token id", tid, ":", e1024.tolist())

uniq = torch.unique(e1024)
print("unique values in e1024", uniq.tolist())

is_binary = torch.all((e1024== 0) | (e1024== 1)).item()
print("is strictly binary (0/1):", is_binary)

# --- 4) Global check: all embedding weights are exactly 0/1 ---
is_binary_global = torch.all((W == 0) | (W == 1)).item()
num_non_binary = torch.numel(W) - torch.sum((W == 0) | (W == 1)).item()
print("is binary globally (0/1):", is_binary_global)
print("non-binary entries:", int(num_non_binary))

Expected output highlights (example):

vocab_size: 65536
config: {'vocab_size': 65536, 'n_embed': 1024, 'd_model': 1024, 'n_layer': 16, 'n_head': 32, 'scale': 1}
token_embeddings.weight shape: (65536, 1024)
text='A'
ids: [65]
tokens: ['A']
1024-dim embedding for token id 65 : [1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 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unique values in e1024 [0.0, 1.0]
is strictly binary (0/1): True
is binary globally (0/1): True
non-binary entries: 0

Intended use

Research-only checkpoint intended for:

Studying emergent semantics when the embedding layer is a frozen random codebook
Comparing glyph-based vs random frozen embeddings under identical training conditions
Controlled interpretability experiments and ablations on embedding initialization/freezing

Not intended for production deployment (no safety/instruction tuning).

🧑‍🔬 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}, 
}

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Emergent Semantics Beyond Token Embeddings

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Paper: 2507.04886 (TMLR, Oct 2025). 'Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations' • 12 items • Updated 7 days ago

Papers for Bochkov/emergent-semantics-model-1024-bit-335m

Growing Transformers: Modular Composition and Layer-wise Expansion on a Frozen Substrate

Paper • 2507.07129 • Published Jul 8, 2025 • 2

Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations

Paper • 2507.04886 • Published Jul 7, 2025 • 2

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