GLM-5.3-Flash-REAP50-GGUF

Model overview

**50% expert-pruned `zai-org/GLM-5.3-Flash` (301B → 157B total, 12.5B active) in GGUF (`IQ3_M` to `Q4_K_M`, 72.9–94.8 GiB) with preserved `Q8_0` MLA + Indexer attention.** **GLM-5.3-Flash-REAP50-GGUF** provides single-file GGUF quantizations of `zai-org/GLM-5.3-Flash` compressed with **REAP-50** (Router-weighted Expert Activation Pruning, retaining **128 of 256 routed experts per MoE layer**). Total parameters drop from **301B to 157B** while active parameters per token remain **12.5B** (top-8 of 128 routed experts + 1 shared expert + Multi-head Latent Attention + Dynamic Sparse Attention Indexer), fitting comfortably inside a single **128 GB unified-memory system** (NVIDIA Jetson AGX Thor, DGX Spark, Mac Studio, or Strix Halo) with up to 202K context. - **50% Routed-Expert Compression (301B → 157B Total, 12.5B Active):** Retains 128 of 256 routed experts across all 75 MoE layers (`layers 3..77`) using a 5-pool domain-balanced REAP saliency calibration (`general`, `code`, `math`, `Chinese`, `tool-use`) with union top-16 specialist protection and frequency-floor guards (`f_j >= 40`). - **Two Weight Vintages Disclosed Transparently (`v2` vs `v3`):** - **`Q4_K_M` (94.8 GiB) & `Q3_K_M` (77.3 GiB) are `v3`** (uploaded 2026-10-20): include the **layer-78 MTP draft head** (`nextn.*` tensors) and the **layer-77 shared-expert output-scale correction (`0.7209x`)** that removes high-entropy tail collapse (`kl_full` reduced from `0.506` to `0.388`). - **`Q4_K_S` (90.0 GiB), `IQ4_XS` (85.6 GiB), & `IQ3_M` (72.9 GiB) are `v2`** (uploaded 2026-10-16): 78-layer (`0..77`) builds without the layer-77 shared-expert scale or layer-78 MTP tensors. - **Uncompromised `Q8_0` Attention & DSA Indexer Path:** Every quantization preserves **all MLA (`wq_a`, `wq_b`, `wkv_a_mqa`, `wkv_b`, `wo`), DSA Indexer (`indexer.wq_b`, `indexer.wk`, `indexer.k_norm`, `indexer.weights_proj`), shared experts (`ffn_*_shexp`), and dense layers (`0..2`) at `Q8_0` or `F32`**, quantizing only the routed expert banks (`ffn_gate_exps`, `ffn_up_exps`, `ffn_down_exps`). - **Paired Held-Out Teacher Evaluation (`262,144` Tokens across 64 Prompts × 4,096 Context):** Evaluates directly against the unpruned 301B `zai-org/GLM-5.3-Flash` teacher: `Q4_K_M` achieves **`0.3204` top-32 KL divergence** and **`83.39%` top-1 argmax agreement** (`88.2%` on math, `86.7%` on Chinese, `86.5%` on code).

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。