huginn-m-fixed-r5
Table 2, M, Fixed R=5 of Towards Looped Models Done Right. Part II: Rethinking at Fixed Points.
[!IMPORTANT] Loading this checkpoint requires the xLLM code. The weights are stored in xLLM's native format (BF16 Safetensors with an xLLM
config.jsonandartifact_manifest.json). This is not a Hugging Facetransformerscheckpoint:AutoModel.from_pretrainedcannot load it. Get the code at https://github.com/ifm-ai/xllm-loop and load the directory withxllm.paper_part2.artifacts.load_artifact.
Model details
| Model | looped Transformer: 1 prelude, 2 recurrent and 1 coda block; width 3,072, 48 heads (12 KV heads), SwiGLU FFN 8,704 |
| Parameters | 819,495,936, including the embedding and output layers |
| Input injection | diagonal |
| Recurrent depth in training | fixed R = 5 |
| Evaluation | R = 5 with terminal KV; the recurrent state is seeded by example identity (seed 42) |
| Training | recipe m_fixed_r5: 20,480 updates of 512 x 8,192 tokens (85.90B), AdamW, warmup-stable-decay |
| Weights | BF16 Safetensors: the training checkpoint's FP32 weights rounded to BF16 |
| Tokenizer | tokenizer/, the Jais64k tokenizer of training |
Download
hf download IFM/LoopedLM-P2-huginn-m-fixed-r5 --local-dir huginn-m-fixed-r5
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
Evaluate with eval_paper_part2.py from the xLLM repository:
ENABLE_FLASH_ATTENTION_3=true python eval_paper_part2.py --artifact huginn-m-fixed-r5 \
--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.
xllm.paper_part2.artifacts.load_artifact("huginn-m-fixed-r5") verifies the directory and returns the model
config fields and the BF16 state dict on CPU.
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.