albertge/dream-7b-dllm-registers-mix60k-t4

dream-7b-dllm-registers-mix60k-t4

Paper: Register Tokens for Bounded-State Reasoning in Diffusion Language Models

Dream-v0-Base-7B finetuned with a discrete 4-token Markovian-Thinking tail as the inter-block carry channel.

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:main_triad (main triad, Dream Markovian Thinking arm)
• —Carry channel: discrete Markovian-Thinking tail (channel_mode=tail, num_registers=0, tail_length=4)
• —Base model: Dream-org/Dream-v0-Base-7B
• —Training data: mix60k (OpenMathInstruct-2 + OpenCodeInstruct, 60K examples)
• —Training config: `SFT/amlt/chunked_sft_mix60k_c128_dream_noresid_bonete54.yaml`
• —Chunk size: C = 128 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/dream-7b-dllm-registers-mix60k-t4", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("albertge/dream-7b-dllm-registers-mix60k-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}
}