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Model: 0xSero/GLM-4.7-REAP-50-W4A16 Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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library_name: transformers
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tags:
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- 4bit
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- MOE
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- autoround
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- cerebras
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- code
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- compression
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- function-calling
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- glm
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- glm4
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- gptq
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- pruning
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- quantized
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- reap
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- w4a16
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license: apache-2.0
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pipeline_tag: text-generation
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base_model:
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- 0xSero/GLM-4.7-185B
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datasets:
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- NeelNanda/pile-10k
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base_model_relation: quantized
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---
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> [!TIP]
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> **[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)
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# GLM-4.7-185B-W4A16
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W4A16 quantization of [0xSero/GLM-4.7-185B](https://huggingface.co/0xSero/GLM-4.7-185B).
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## At a glance
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|---|---|
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| Base model | [0xSero/GLM-4.7-185B](https://huggingface.co/0xSero/GLM-4.7-185B) |
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| Format | W4A16 |
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| Total params | **185B** |
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| Active / token | — |
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| Experts / layer | 80 |
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| Layers | 92 |
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| Hidden size | 5120 |
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| Context | 202,752 |
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| On-disk size | 99 GB |
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## Which variant should I pick?
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| Variant | Format | Link |
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|---|---|---|
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| `GLM-4.7-185B` | BF16 | [link](https://huggingface.co/0xSero/GLM-4.7-185B) |
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| `GLM-4.7-185B-W4A16` **(this)** | W4A16 | [link](https://huggingface.co/0xSero/GLM-4.7-185B-W4A16) |
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| `GLM-4.7-202B` | BF16 | [link](https://huggingface.co/0xSero/GLM-4.7-202B) |
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| `GLM-4.7-218B-W4A16` | W4A16 | [link](https://huggingface.co/0xSero/GLM-4.7-218B-W4A16) |
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| `GLM-4.7-REAP-40-W4A16` | W4A16 | [link](https://huggingface.co/0xSero/GLM-4.7-REAP-40-W4A16) |
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<p align="center">
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<em>𓌳 <strong>REAP</strong>𓌳 the Experts: Why Pruning Prevails for One-Shot MoE Compression</em><br>
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<a href="https://arxiv.org/abs/2510.13999">📄 Paper</a> • <a href="https://github.com/CerebrasResearch/reap">💻 Code</a> • <a href="https://www.cerebras.ai/blog/reap">📝 Blog</a>
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</p>
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# GLM-4.7-REAP-50-W4A16
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## ✨ Highlights
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**50% Expert-Pruned + INT4 Quantized** — Double compression for efficient deployment.
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- **~6.5x Total Compression**: 700GB → ~92GB
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- **REAP + AutoRound**: Expert pruning + weight quantization
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- **Optimized for Code & Tools**: Calibrated on code generation and function calling
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- **Lower VRAM**: Fits on 2-4x fewer GPUs than BF16
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## 📋 Model Specifications
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| Property | Value |
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|----------|-------|
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| **Base Model** | [GLM-4.7-REAP-50](https://huggingface.co/0xSero/GLM-4.7-185B) |
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| **Original (GLM-4.7)** | 358B params, ~700GB |
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| **After REAP 50%** | 179B params |
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| **After W4A16 Quant** | ~92GB on disk |
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| **Quantization** | INT4 weights, FP16 activations |
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| **Group Size** | 128 |
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| **Format** | GPTQ (AutoRound) |
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| **Experts per Layer** | 80 (was 160) |
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| **VRAM Required** | ~100GB |
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### Compression Pipeline
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```
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GLM-4.7 (358B, 700GB)
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│
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▼ REAP 50% expert pruning
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│
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GLM-4.7-REAP-50 (179B)
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│
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▼ AutoRound W4A16 quantization
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│
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GLM-4.7-REAP-50-W4A16 (~92GB) ◀── This model
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Total: ~6.5x compression
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```
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---
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## 🔬 Calibration Dataset: Deep Dive
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REAP's effectiveness depends critically on **calibration data that represents the target use case**. We specifically optimized for **code generation**, **function/tool calling**, and **agentic workflows**.
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### Why These 3 Datasets?
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| Dataset | Samples | Purpose | Why It Matters |
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|---------|---------|---------|----------------|
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| [evol-codealpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) | 700 | Code generation | **51% of mix** — Code tasks activate specific expert pathways; pruning without code calibration destroys coding ability |
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| [xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) | 330 | Function/tool calling | **24% of mix** — Tool use requires structured JSON output; experts handling schema generation must be preserved |
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| [SWE-smith-trajectories](https://huggingface.co/datasets/SWE-bench/SWE-smith-trajectories) | 330 | Agentic multi-turn | **24% of mix** — Real SWE-bench trajectories with tool calls, file edits, and multi-step reasoning |
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### The Science Behind Dataset Selection
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```
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REAP Algorithm:
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1. Forward pass calibration samples through model
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2. Record which experts activate and their magnitudes
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3. Compute saliency = router_weight × activation_norm
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4. Prune lowest-saliency experts
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Key Insight: Experts are TASK-SPECIFIC
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├── Some experts specialize in natural language
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├── Some experts specialize in code syntax
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├── Some experts specialize in JSON/structured output
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└── Some experts specialize in multi-turn context
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If calibration lacks code → code-specialized experts appear "unused" → get pruned → model loses coding ability
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```
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### Cerebras' Original Mix (from paper)
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Cerebras used the same 3 datasets in their GLM-4.6 REAP experiments:
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- evol-codealpaca-v1 for code generation
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- xlam-function-calling-60k for tool calling
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- SWE-smith-trajectories for agentic tasks
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We followed this exact recipe for reproducibility.
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### Combined Dataset
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Our calibration mix: [0xSero/glm47-reap-calibration-v2](https://huggingface.co/datasets/0xSero/glm47-reap-calibration-v2)
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---
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## 🚀 Deployment
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### vLLM (Recommended)
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```bash
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vllm serve 0xSero/GLM-4.7-185B-W4A16 \
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--tensor-parallel-size 4 \
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--trust-remote-code \
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--quantization gptq
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```
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### Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"0xSero/GLM-4.7-185B-W4A16",
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device_map="auto",
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trust_remote_code=True
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)
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tokenizer = AutoTokenizer.from_pretrained("0xSero/GLM-4.7-185B-W4A16", trust_remote_code=True)
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```
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---
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## 🧩 Reproduction
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### Step 1: REAP Pruning
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```python
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#!/usr/bin/env python3
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"""
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REAP Pruning Script for MoE Models
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Adapted from: https://github.com/CerebrasResearch/reap
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"""
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import subprocess
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import sys
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def run_reap(
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model_path: str,
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compression_ratio: float,
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dataset: str = "0xSero/glm47-reap-calibration-v2",
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samples: int = 1360,
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seed: int = 42,
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distance: str = "angular",
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reuse_observations: str = None,
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):
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"""
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Run REAP expert pruning.
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Args:
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model_path: Path to base model
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compression_ratio: 0.30 = prune 30%, keep 70%
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dataset: Calibration dataset (code + tools + agentic)
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samples: Number of calibration samples
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seed: Random seed for reproducibility
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distance: Distance metric for expert clustering
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reuse_observations: Path to pre-computed observations for instant pruning
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"""
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cmd = [
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sys.executable, "src/reap/prune.py",
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"--model-name", model_path,
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"--dataset-name", dataset,
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"--compression-ratio", str(compression_ratio),
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"--prune-method", "reap",
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"--seed", str(seed),
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"--samples_per_category", str(samples),
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"--model_max_length", "2048",
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"--distance_measure", distance,
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"--record_pruning_metrics_only", "true",
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]
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if reuse_observations:
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# Instant pruning: skip calibration, reuse precomputed expert scores
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cmd.extend(["--load_observations", reuse_observations])
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subprocess.run(cmd, check=True)
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# Example: Create 40% pruned model
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run_reap(
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model_path="/path/to/GLM-4.7",
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compression_ratio=0.40, # Prune 40% of experts
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)
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```
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### Step 2: AutoRound Quantization
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```python
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#!/usr/bin/env python3
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"""
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AutoRound W4A16 Quantization
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Intel's state-of-the-art weight quantization using signed gradient descent.
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"""
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from auto_round import AutoRound
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def quantize_w4a16(
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model_path: str,
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output_dir: str,
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bits: int = 4,
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group_size: int = 128,
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format: str = "auto_gptq",
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):
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"""
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Quantize model to INT4 weights with FP16 activations.
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Args:
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model_path: Path to REAP-pruned model
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output_dir: Output directory
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bits: Weight bit width (4 for W4A16)
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group_size: Quantization group size (128 is optimal)
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format: Output format (auto_gptq for vLLM compatibility)
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"""
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ar = AutoRound(
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model_path,
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scheme="W4A16",
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device="cuda",
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device_map="auto",
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trust_remote_code=True,
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batch_size=1,
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seqlen=512,
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nsamples=64,
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)
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ar.quantize_and_save(output_dir, format=format)
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# Example: Quantize REAP-40 to W4A16
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quantize_w4a16(
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model_path="./GLM-4.7-REAP-40",
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output_dir="./GLM-4.7-REAP-40-W4A16",
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)
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```
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---
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## ⚖️ License
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Apache 2.0
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---
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## License & citation
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License inherited from the base model.
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```bibtex
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@misc{lasby2025reap,
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title = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
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author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
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year = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
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}
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```
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## Sponsors
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Made possible by **NVIDIA · TNG Technology · Lambda · Prime Intellect · Hot Aisle**.
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