best_bvv_unfrozen_zh

📚 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) - 💻 Code

Model summary

best_bvv_unfrozen_zh is a 500M parameter Causal Language Model (LM) trained as an open proof-of-concept for the "frozen embeddings" paradigm. This version uses fully trainable token embeddings – a standard setup – and serves as a baseline for direct comparison with the corresponding "frozen-embedding" model Bochkov/best_bvv_zh.

Architecture: Transformer, rotary positional encoding Vocabulary: Custom Unicode-based, 131072 tokens Embedding: Unfrozen (trainable, classic) Pretraining data: 9B tokens, (Wikipedia, SQuAD2.0, TriviaQA, NQ etc) and 10% SFT (instruction/factual Q&A) mixed in Purpose: Compare learning capacity and generalization of full vs. frozen-embedding LMs on small data

Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained('Bochkov/best_bvv_unfrozen_zh', trust_remote_code=True).to('cuda')
tokenizer = AutoTokenizer.from_pretrained('Bochkov/best_bvv_unfrozen_zh')
inputs = tokenizer("Hello, world! ", return_tensors="pt").to('cuda')
outputs = model.generate(
    **inputs, 
    max_new_tokens=100, 
    temperature=0.8, 
    top_k=50, 
    top_p=0.95, 
    do_sample=True
)
print(tokenizer.decode(outputs[0]))

Citation

If you find this work helpful or inspiring, please consider citing the associated papers:

@misc{bochkov2025emergentsemanticstokenembeddings,
      title={Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations}, 
      author={A. Bochkov},
      year={2025},
      eprint={2507.04886},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.04886}, 
}

@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}, 
}