albertge/llada-8b-dllm-registers-code64-codetags-t4

llada-8b-dllm-registers-code64-codetags-t4

Continued chunked SFT from albertge/llada-8b-dllm-registers-mix60k-t4 on the code-only split of mix60k at C=64 with <code>-delimited targets.

This checkpoint is part of the dLLM Registers project — register tokens as a bounded, trained, continuous channel for carrying decoding state across denoising windows in diffusion language models.

• —Paper section: tab:code_chunk64 (Markovian Thinking arm)
• —Carry channel: discrete Markovian-Thinking tail (channel_mode=tail, num_registers=0, tail_length=4)
• —Base model: GSAI-ML/LLaDA-8B-Base
• —Training data: OpenCodeInstruct subset of mix60k (~30K traces), <code>...</code> targets
• —Training config: `SFT/amlt/chunked_sft_mix60k_code64_code_tags_continuation_bonete54.yaml`
• —Chunk size: C = 64 tokens
• —Prompt dropout rate (Bernoulli per-trace CSG mask): 0.0
• —Date uploaded: 2026-06-13

How to load

from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("albertge/llada-8b-dllm-registers-code64-codetags-t4", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("albertge/llada-8b-dllm-registers-code64-codetags-t4", trust_remote_code=True)

To use the carry channel correctly at inference, see the evaluator at `eval/eval.py` and the wrapper `eval/run_mix60k_full_eval.sh`. Key flags for this checkpoint: --num_registers 0 --channel_mode tail --tail_length 4

Repository

Training and eval code: https://github.com/lbertge/d1-registers

Citation

If you use this checkpoint, please cite the dLLM Registers paper:

@misc{dllm-registers-2026,
  title  = {Register Tokens for Unbounded Reasoning in Diffusion Language Models},
  author = {Albert Ge and collaborators},
  year   = {2026},
  note   = {Preprint},
  url    = {https://github.com/lbertge/d1-registers}
}