GLM-4.7-REAP-50-W4A16/README.md

---
language:
- en
library_name: transformers
tags:
- 4bit
- MOE
- autoround
- cerebras
- code
- compression
- function-calling
- glm
- glm4
- gptq
- pruning
- quantized
- reap
- w4a16
license: apache-2.0
pipeline_tag: text-generation
base_model:
- 0xSero/GLM-4.7-185B
datasets:
- NeelNanda/pile-10k
base_model_relation: quantized
---

> [!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-4.7-185B-W4A16

W4A16 quantization of [0xSero/GLM-4.7-185B](https://huggingface.co/0xSero/GLM-4.7-185B).

## At a glance

| | |
|---|---|
| Base model | [0xSero/GLM-4.7-185B](https://huggingface.co/0xSero/GLM-4.7-185B) |
| Format | W4A16 |
| Total params | **185B** |
| Active / token | — |
| Experts / layer | 80 |
| Layers | 92 |
| Hidden size | 5120 |
| Context | 202,752 |
| On-disk size | 99 GB |

## Which variant should I pick?

| Variant | Format | Link |
|---|---|---|
| `GLM-4.7-185B` | BF16 | [link](https://huggingface.co/0xSero/GLM-4.7-185B) |
| `GLM-4.7-185B-W4A16` **(this)** | W4A16 | [link](https://huggingface.co/0xSero/GLM-4.7-185B-W4A16) |
| `GLM-4.7-202B` | BF16 | [link](https://huggingface.co/0xSero/GLM-4.7-202B) |
| `GLM-4.7-218B-W4A16` | W4A16 | [link](https://huggingface.co/0xSero/GLM-4.7-218B-W4A16) |
| `GLM-4.7-REAP-40-W4A16` | W4A16 | [link](https://huggingface.co/0xSero/GLM-4.7-REAP-40-W4A16) |

<p align="center">
  <em>𓌳 <strong>REAP</strong>𓌳  the Experts: Why Pruning Prevails for One-Shot MoE Compression</em><br>
  <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>
</p>

# GLM-4.7-REAP-50-W4A16

## ✨ Highlights

**50% Expert-Pruned + INT4 Quantized** — Double compression for efficient deployment.

- **~6.5x Total Compression**: 700GB → ~92GB
- **REAP + AutoRound**: Expert pruning + weight quantization
- **Optimized for Code & Tools**: Calibrated on code generation and function calling
- **Lower VRAM**: Fits on 2-4x fewer GPUs than BF16


## 📋 Model Specifications

| Property | Value |
|----------|-------|
| **Base Model** | [GLM-4.7-REAP-50](https://huggingface.co/0xSero/GLM-4.7-185B) |
| **Original (GLM-4.7)** | 358B params, ~700GB |
| **After REAP 50%** | 179B params |
| **After W4A16 Quant** | ~92GB on disk |
| **Quantization** | INT4 weights, FP16 activations |
| **Group Size** | 128 |
| **Format** | GPTQ (AutoRound) |
| **Experts per Layer** | 80 (was 160) |
| **VRAM Required** | ~100GB |

### Compression Pipeline

```
GLM-4.7 (358B, 700GB)
        │
        ▼  REAP 50% expert pruning
        │
GLM-4.7-REAP-50 (179B)
        │
        ▼  AutoRound W4A16 quantization
        │
GLM-4.7-REAP-50-W4A16 (~92GB)  ◀── This model

Total: ~6.5x compression
```

---


## 🔬 Calibration Dataset: Deep Dive

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**.

### Why These 3 Datasets?

| Dataset | Samples | Purpose | Why It Matters |
|---------|---------|---------|----------------|
| [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 |
| [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 |
| [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 |

### The Science Behind Dataset Selection

```
REAP Algorithm:
1. Forward pass calibration samples through model
2. Record which experts activate and their magnitudes
3. Compute saliency = router_weight × activation_norm
4. Prune lowest-saliency experts

Key Insight: Experts are TASK-SPECIFIC
├── Some experts specialize in natural language
├── Some experts specialize in code syntax
├── Some experts specialize in JSON/structured output
└── Some experts specialize in multi-turn context

If calibration lacks code → code-specialized experts appear "unused" → get pruned → model loses coding ability
```

### Cerebras' Original Mix (from paper)

Cerebras used the same 3 datasets in their GLM-4.6 REAP experiments:
- evol-codealpaca-v1 for code generation
- xlam-function-calling-60k for tool calling
- SWE-smith-trajectories for agentic tasks

We followed this exact recipe for reproducibility.

### Combined Dataset

Our calibration mix: [0xSero/glm47-reap-calibration-v2](https://huggingface.co/datasets/0xSero/glm47-reap-calibration-v2)


---

## 🚀 Deployment

### vLLM (Recommended)

```bash
vllm serve 0xSero/GLM-4.7-185B-W4A16 \
    --tensor-parallel-size 4 \
    --trust-remote-code \
    --quantization gptq
```

### Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "0xSero/GLM-4.7-185B-W4A16",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("0xSero/GLM-4.7-185B-W4A16", trust_remote_code=True)
```

---

## 🧩 Reproduction

### Step 1: REAP Pruning


```python
#!/usr/bin/env python3
"""
REAP Pruning Script for MoE Models
Adapted from: https://github.com/CerebrasResearch/reap
"""

import subprocess
import sys

def run_reap(
    model_path: str,
    compression_ratio: float,
    dataset: str = "0xSero/glm47-reap-calibration-v2",
    samples: int = 1360,
    seed: int = 42,
    distance: str = "angular",
    reuse_observations: str = None,
):
    """
    Run REAP expert pruning.

    Args:
        model_path: Path to base model
        compression_ratio: 0.30 = prune 30%, keep 70%
        dataset: Calibration dataset (code + tools + agentic)
        samples: Number of calibration samples
        seed: Random seed for reproducibility
        distance: Distance metric for expert clustering
        reuse_observations: Path to pre-computed observations for instant pruning
    """
    cmd = [
        sys.executable, "src/reap/prune.py",
        "--model-name", model_path,
        "--dataset-name", dataset,
        "--compression-ratio", str(compression_ratio),
        "--prune-method", "reap",
        "--seed", str(seed),
        "--samples_per_category", str(samples),
        "--model_max_length", "2048",
        "--distance_measure", distance,
        "--record_pruning_metrics_only", "true",
    ]

    if reuse_observations:
        # Instant pruning: skip calibration, reuse precomputed expert scores
        cmd.extend(["--load_observations", reuse_observations])

    subprocess.run(cmd, check=True)

# Example: Create 40% pruned model
run_reap(
    model_path="/path/to/GLM-4.7",
    compression_ratio=0.40,  # Prune 40% of experts
)
```


### Step 2: AutoRound Quantization


```python
#!/usr/bin/env python3
"""
AutoRound W4A16 Quantization
Intel's state-of-the-art weight quantization using signed gradient descent.
"""

from auto_round import AutoRound

def quantize_w4a16(
    model_path: str,
    output_dir: str,
    bits: int = 4,
    group_size: int = 128,
    format: str = "auto_gptq",
):
    """
    Quantize model to INT4 weights with FP16 activations.

    Args:
        model_path: Path to REAP-pruned model
        output_dir: Output directory
        bits: Weight bit width (4 for W4A16)
        group_size: Quantization group size (128 is optimal)
        format: Output format (auto_gptq for vLLM compatibility)
    """
    ar = AutoRound(
        model_path,
        scheme="W4A16",
        device="cuda",
        device_map="auto",
        trust_remote_code=True,
        batch_size=1,
        seqlen=512,
        nsamples=64,
    )
    ar.quantize_and_save(output_dir, format=format)

# Example: Quantize REAP-40 to W4A16
quantize_w4a16(
    model_path="./GLM-4.7-REAP-40",
    output_dir="./GLM-4.7-REAP-40-W4A16",
)
```


---

## ⚖️ License

Apache 2.0

---

## License & citation
License inherited from the base model.

```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}
}
```

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