Upload README.md with huggingface_hub

README.md

@@ -11,6 +11,9 @@ tags:
   - expert-pruning
 ---
 
+> [!TIP]
+> **[Support this work →](https://donate.sybilsolutions.ai)** · [X](https://x.com/0xsero) · [GitHub](https://github.com/0xsero) · [REAP paper](https://arxiv.org/abs/2510.13999) · [Cerebras REAP](https://huggingface.co/collections/cerebras/cerebras-reap)
+
 # GLM-5.3-569B — REAP keep-192 (EXL3 3.0bpw)
 
 > A **25%-expert-pruned GLM-5.3** (192 / 256 routed experts per layer, ~569B params), quantized to
@@ -44,3 +47,27 @@ agreement 0.822 vs 0.775, KLD cut by a third.
 | [`GLM-5.3-500B-EXL3-3.0bpw`](https://huggingface.co/0xSero/GLM-5.3-500B-EXL3-3.0bpw) | 168 | 197 GB | 0.511 |
 
 Method and the full criterion study: [`0xSero/glm-5.3-reap-fidelity-study`](https://huggingface.co/datasets/0xSero/glm-5.3-reap-fidelity-study).
+
+---
+
+## Acknowledgements
+
+- **[Z.AI / zai-org](https://huggingface.co/zai-org)** for [GLM-5.3](https://huggingface.co/zai-org/GLM-5.3), the base model.
+- **[Cerebras Research](https://github.com/CerebrasResearch/reap)** for REAP (Router-weighted Expert Activation Pruning), the pruning method — [arXiv:2510.13999](https://arxiv.org/abs/2510.13999).
+- **[turboderp / exllamav3](https://github.com/turboderp-org/exllamav3)** for the EXL3 trellis quantization format and runtime.
+
+Observations: [`0xSero/glm-5.3-nvfp4-reap-observations-v1`](https://huggingface.co/datasets/0xSero/glm-5.3-nvfp4-reap-observations-v1). Fidelity study: [`0xSero/glm-5.3-reap-fidelity-study`](https://huggingface.co/datasets/0xSero/glm-5.3-reap-fidelity-study). Pruning, quantization, and evaluation ran on 8× NVIDIA RTX PRO 6000 Blackwell.
+
+## Citation
+
+```bibtex
+@misc{lasby2025reap,
+  title  = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
+  author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
+  year   = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
+}
+```
+
+## License
+
+Inherited from the base model — see the [GLM-5.3 model card](https://huggingface.co/zai-org/GLM-5.3) for terms.