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Model: MuXodious/GLM-4.7-Flash-REAP-23B-A3B-absolute-heresy-GGUF
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README.md

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+---
+language:
+- en
+library_name: transformers
+tags:
+- glm
+- MOE
+- pruning
+- compression
+- heretic
+- uncensored
+- decensored
+- abliterated
+license: mit
+name: cerebras/GLM-4.7-Flash-REAP-23B-A3B
+description: >
+  This model was obtained by uniformly pruning 25% of experts in GLM-4.7-Flash
+  using the REAP method.
+readme: |
+  https://huggingface.co/cerebras/GLM-4.7-Flash-REAP-23B-A3B/main/README.md
+license_link: https://huggingface.co/zai-org/GLM-4.7-Flash/blob/main/LICENSE
+pipeline_tag: text-generation
+base_model:
+- MuXodious/GLM-4.7-Flash-REAP-23B-A3B-absolute-heresy
+---
+Static GGUF quants of **GLM-4.7-Flash-REAP-23B-A3B-absolute-heresy**.
+
+---
+This is a **GLM-4.7-Flash-REAP-23B-A3B** fine-tune, produced at the request of [McG-221](https://huggingface.co/McG-221) through P-E-W's [Heretic](https://github.com/p-e-w/heretic) (v1.1.0) abliteration engine merged with the [Magnitude-Preserving Orthogonal Ablation PR](https://github.com/p-e-w/heretic/pull/52).
+
+**Note:** *Transformers v5.0.0 or higher is required to interface.*
+
+---
+<img src="https://img.shields.io/badge/HERESY_INDEX-ABSOLUTE-white?style=flat-square&labelColor=101010" align="right" width="250">
+
+**Heretication Results**
+
+| Score Metric | Value | Parameter | Value |
+| :--- | :--- | :--- | :--- |
+| **Refusals** | 4/100 | **direction_index** | 21.21 |
+| **KL Divergence** | 0.0054| **attn.o_proj.max_weight** | 1.99  |
+| **Initial Refusals** | 92/100 | **attn.o_proj.max_weight_position** | 29.07 |
+||| **attn.o_proj.min_weight** | 1.30 |
+||| **attn.o_proj.min_weight_distance** | 12.21 |
+||| **mlp.down_proj.max_weight** | 1.39 |
+||| **mlp.down_proj.max_weight_position** | 30.10 |
+||| **mlp.down_proj.min_weight** | 1.09 |
+||| **mlp.down_proj.min_weight_distance** | 2.82 |
+
+---
+## Degree of Heretication
+The **Heresy Index** weighs the resulting model's corruption by the process (KL Divergence) and its abolition of doctrine (Refusals) for a final verdict in classification.
+
+| Index Entry | Classification | Analysis |
+| :--- | :--- | :--- |
+| ![Absolute](https://img.shields.io/badge/HERESY_INDEX-ABSOLUTE-white?style=flat-square&labelColor=101010) | **Absolute Heresy** | Less than 10/100 Refusals and 0.10 KL Divergence |
+| ![Tainted](https://img.shields.io/badge/HERESY_INDEX-TAINTED-blueviolet?style=flat-square&labelColor=101010) | **Tainted Heresy** | Around 25-11/100 Refusals and/or -0.20-0.11 KL Divergence |
+| ![Impotent](https://img.shields.io/badge/HERESY_INDEX-IMPOTENT-5c4033?style=flat-square&labelColor=101010) | **Impotent Heresy** | Anything above 25/100 Refusals and 0.21 KL Divergence |
+
+**Note**: This is an arbitrary classification inspired by Warhammer 40K, having no tangible indication towards the model's performance.
+
+---
+<p align="center">
+  <em>𓌳 <strong>REAP</strong>𓌳  the Experts: Why Pruning Prevails for One-Shot MoE Compression</em><br>
+  <img src="https://i.imgur.com/rmzG3gg.png" alt="REAP" width="75%">
+</p>
+
+# GLM-4.7-Flash-REAP-23B-A3B
+
+## ✨ Highlights
+
+Introducing **GLM-4.7-Flash-REAP-23B-A3B**, a **memory-efficient compressed variant** of GLM-4.7-Flash that maintains near-identical performance while being **25% lighter**.
+
+This model was created using **REAP (Router-weighted Expert Activation Pruning)**, a novel expert pruning method that selectively removes redundant experts while preserving the router's independent control over remaining experts. Key features include:
+
+- **Near-Lossless Performance**: Maintains almost identical accuracy on code generation, agentic coding, and function calling tasks compared to the full 355B model
+- **25% Memory Reduction**: Compressed from 355B to 218B parameters, significantly lowering deployment costs and memory requirements
+- **Preserved Capabilities**: Retains all core functionalities including code generation, agentic workflows, repository-scale understanding, and function calling
+- **Drop-in Compatibility**: Works with vanilla vLLM - no source modifications or custom patches required
+- **Optimized for Real-World Use**: Particularly effective for resource-constrained environments, local deployments, and academic research
+
+---
+## 📋 Model Overview
+
+**GLM-4.7-Flash-REAP-23B-A3B** has the following specifications:
+
+- **Base Model**: GLM-4.7-Flash
+- **Compression Method**: REAP (Router-weighted Expert Activation Pruning)
+- **Compression Ratio**: 25% expert pruning
+- **Type**: Sparse Mixture-of-Experts (SMoE) Causal Language Model
+- **Number of Parameters**: 23B total, 3B activated per token
+- **Number of Layers**: 47
+- **Number of Attention Heads**: 20 for QKV
+- **Number of Experts**: 48 (uniformly pruned from 64)
+- **Number of Activated Experts**: 4 per token
+- **Context Length**: 202,752 tokens
+- **License**: MIT
+
+---
+
+## 📊 Evaluations
+
+<table>
+  <thead>
+    <tr>
+      <th align="left">Benchmark</th>
+      <th align="center">GLM-4.7-Flash</th>
+      <th align="center"><a href="https://huggingface.co/cerebras/GLM-4.7-Flash-REAP-23B-A3B">GLM-4.7-Flash-REAP-23B-A3B</a></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>Compression</strong></td>
+      <td align="center">—</td>
+      <td align="center">25%</td>
+    </tr>
+    <tr>
+      <td colspan="5" align="center"><strong>Coding</strong></td>
+    </tr>
+    <tr>
+      <td><strong>HumanEval</strong></td>
+      <td align="center">94.5</td>
+      <td align="center">95.1</td>
+    </tr>
+    <tr>
+      <td><strong>HumanEval+</strong></td>
+      <td align="center">89.0</td>
+      <td align="center">89.0</td>
+    </tr>
+</table>
+
+🟩 *This checkpoint maintains almost identical performance while being 25% lighter.*
+
+For more details on the evaluation setup, refer to the [REAP arXiv preprint](https://arxiv.org/abs/2510.13999).
+
+---
+
+## 🚀 Deployment
+
+You can deploy the model directly using the **latest vLLM** (that supports GLM4.7-Flash), no source modifications or custom patches required.
+
+```bash
+vllm serve cerebras/GLM-4.7-Flash-REAP-23B-A3B \
+    --tensor-parallel-size 4 \
+    --reasoning-parser glm45 \
+    --tool-call-parser glm47 \
+    --enable-auto-tool-choice
+```
+
+If you encounter insufficient memory when running this model, you might need to set a lower value for `--max-num-seqs` flag (e.g. set to 64).
+
+
+## 🧩 Model Creation
+
+This checkpoint was created by applying the **REAP (Router-weighted Expert Activation Pruning)** method uniformly across all Mixture-of-Experts (MoE) blocks of **GLM-4.7**, with a **25% pruning rate**.
+
+### How REAP Works
+
+REAP selects experts to prune based on a novel **saliency criterion** that considers both:
+- **Router gate values**: How frequently and strongly the router activates each expert
+- **Expert activation norms**: The magnitude of each expert's output contributions
+
+This dual consideration ensures that experts contributing minimally to the layer's output are pruned, while preserving those that play critical roles in the model's computations.
+
+### Key Advantages
+
+- **One-Shot Compression**: No fine-tuning required after pruning - the model is immediately ready for deployment
+- **Preserved Router Control**: Unlike expert merging methods, REAP maintains the router's independent, input-dependent control over remaining experts, avoiding "functional subspace collapse"
+- **Generative Task Superiority**: REAP significantly outperforms expert merging approaches on generative benchmarks (code generation, creative writing, mathematical reasoning) while maintaining competitive performance on discriminative tasks
+
+### Calibration
+
+The model was calibrated using a diverse mixture of domain-specific datasets including:
+- Code generation samples ([evol-codealpaca](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1))
+- Function calling examples ([xlam-function-calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k))
+- Agentic multi-turn trajectories ([SWE-smith-trajectories](https://huggingface.co/datasets/SWE-bench/SWE-smith-trajectories))
+
+📚 For more details, refer to the following resources:
+
+- [🧾 arXiv Preprint](https://arxiv.org/abs/2510.13999)
+- [🧾 REAP Blog](https://www.cerebras.ai/blog/reap)
+- [💻 REAP Codebase (GitHub)](https://github.com/CerebrasResearch/reap)
+
+---
+
+## ⚖️ License
+
+This model is derived from
+**[`zai-org/GLM-4.7-Flash`](https://huggingface.co/zai-org/GLM-4.7-Flash)**
+and distributed under the **MIT license**.
+
+---
+
+## 🧾 Citation
+
+If you use this checkpoint, please cite the REAP paper:
+
+```bibtex
+@article{lasby-reap,
+  title={REAP the Experts: Why Pruning Prevails for One-Shot MoE compression},
+  author={Lasby, Mike and Lazarevich, Ivan and Sinnadurai, Nish and Lie, Sean and Ioannou, Yani and Thangarasa, Vithursan},
+  journal={arXiv preprint arXiv:2510.13999},
+  year={2025}
+}
+```