Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- language-model
+- flow-matching
+- diffusion
+- hypersphere
+- discrete-diffusion
+datasets:
+- tinygsm
+- openwebtext
+library_name: pytorch
+---
+
+# Language Modeling with Hyperspherical Flows
+
+By [Justin Deschenaux](https://jdeschena.com) and [Caglar Gulcehre](https://www.caglar.ai).
+
+[![arXiv](https://img.shields.io/badge/arXiv-2605.11125-red.svg)](https://arxiv.org/abs/2605.11125)
+[![Blog](https://img.shields.io/badge/Blog%20%20-8A2BE2)](https://jdeschena.com/blog/sfm)
+[![Code](https://img.shields.io/badge/Code-181717?logo=github&logoColor=white)](https://github.com/jdeschena/s-flm)
+
+This repo hosts the pretrained checkpoints for **Language Modeling with Hyperspherical Flows** (𝕊-FLM). For the abstract, training/sampling code, and reproduction scripts, see the companion code repo: [`jdeschena/s-flm`](https://github.com/jdeschena/s-flm).
+
+# Checkpoints
+
+𝕊-FLM and the baselines we compare against (AR, MDLM, Duo, FLM, CANDI), trained on **TinyGSM** (250k steps, SmolLM-135M tokenizer) and **OpenWebText** (1M steps, GPT-2 tokenizer).
+
+```
+tinygsm/{ar,mdlm,duo}.ckpt
+tinygsm/candi/{lr3e-4,lr1e-3}.ckpt
+tinygsm/flm/{default,caps}.ckpt
+tinygsm/sfm/{sphere_dit_truncated_fixed_no_renorm,
+             sphere_dit_truncated_adaptive_no_renorm,
+             sphere_arch_truncated_adaptive_no_renorm}.ckpt
+
+owt/{ar,mdlm,duo,flm,sfm}.ckpt
+```
+
+```bash
+huggingface-cli download jdeschena/s-flm tinygsm/duo.ckpt --local-dir ./checkpoints
+```
+
+Loading and sampling are handled by the code repo — see [`jdeschena/s-flm`](https://github.com/jdeschena/s-flm) for the scripts.
+
+# Citation
+
+```
+@misc{deschenaux2026languagemodelinghypersphericalflows,
+      title={Language Modeling with Hyperspherical Flows}, 
+      author={Justin Deschenaux and Caglar Gulcehre},
+      year={2026},
+      eprint={2605.11125},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2605.11125}, 
+}
+```