distilled-m-teacher-init

Table 4, M, Distilled, teacher init of Towards Looped Models Done Right. Part II: Rethinking at Fixed Points: a distilled prefill path for huginn-m-learned-entropy0p01.

Loading this checkpoint requires the xLLM code. The weights are stored in xLLM's native format (BF16 Safetensors with an xLLM config.json and artifact_manifest.json). This is not a Hugging Face transformers checkpoint: AutoModel.from_pretrained cannot load it. Get the code at https://github.com/ifm-ai/xllm-loop and load it with its teacher through xllm.paper_part2.distill.load_student.

Model details


Model	a d x d input map and 2 Transformer blocks of the teacher's width
Parameters	217,067,520
Teacher	`huginn-m-learned-entropy0p01`
Initialization	from the teacher: the input map is its injection at a zero state, the blocks copy its recurrent blocks
Target	the teacher's pre-coda state after 5 recurrences, from its prelude output
Loss	hidden-state MSE over the target energy, plus KL from the teacher's next-token distribution
Training	recipe `m_distill_teacher_init`: 10,240 updates of 256 x 8,192 tokens, AdamW (0.9, 0.95), cosine schedule
Prefill	the student's state, one teacher recurrence and the teacher's coda write the four KV banks; the teacher decodes at R = 5
Weights	BF16 Safetensors: the trained FP32 weights rounded to BF16

The paper initialized this student from the teacher's FP32 weights. Retraining it from the released BF16 teacher starts from BF16-rounded copies.

Download

hf download IFM/LoopedLM-P2-huginn-m-learned-entropy0p01 --local-dir huginn-m-learned-entropy0p01
hf download IFM/LoopedLM-P2-distilled-m-teacher-init --local-dir distilled-m-teacher-init

The xLLM loader checks the directory against artifact_manifest.json: it rejects symbolic links and files the manifest does not list, apart from the .gitattributes file and the .cache/huggingface/ folder that hf download --local-dir adds. Download into a directory as above, not into the Hub cache (~/.cache/huggingface/hub), whose files are symbolic links.

Use

A distilled student is evaluated together with its teacher, which supplies the prelude, one recurrence and the coda. Download both, then run eval_paper_part2.py from the xLLM repository:

ENABLE_FLASH_ATTENTION_3=true python eval_paper_part2.py --artifact huginn-m-learned-entropy0p01 --student distilled-m-teacher-init \
    --data /path/to/eval-data/data.json --out out ppl

data.json and the evaluation inputs come from release/paper-part2/prepare-eval-data.py --output /path/to/eval-data.

The student's config.json pins its teacher's manifest_sha256, the digest recorded in the teacher's artifact_manifest.json (see Provenance); xllm.paper_part2.distill.load_student refuses any other teacher artifact, so use the teacher repository at the matching revision.

Provenance

Teacher manifest_sha256: 766ad24ef935ff5acb4fb37470347371d00c15dedbdcf0d99613ac37ac3bcaf6

artifact_manifest.json records the size and SHA-256 of every file in this repository.

Paper and citation

Towards Looped Models Done Right. Part II: Rethinking at Fixed Points: https://arxiv.org/abs/2610.06833

@article{huang2026fixedpoints,
  title   = {Towards Looped Models Done Right, Part II: Rethinking at Fixed Points},
  author  = {Benhao Huang and Chufan Shi and Junlin Chen and Shicheng Wen and Zhengzhong Liu and Eric Xing and Xuezhe Ma},
  journal = {arXiv preprint arXiv:2610.06833},
  year    = {2026}
}

License

The weights are released under the Apache License 2.0 (LICENSE); NOTICE records the tokenizer's attribution and how the artifact was prepared. The xLLM code is distributed under its own license.