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.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Qwen3.8-Flash-Next-UD-Q3_K_XL-reap320-00002-of-00002.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen3.8-Flash-Next-UD-Q3_K_XL-reap320-00001-of-00002.gguf filter=lfs diff=lfs merge=lfs -text

Qwen3.8-Flash-Next-UD-Q3_K_XL-reap320-00001-of-00002.gguf

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Qwen3.8-Flash-Next-UD-Q3_K_XL-reap320-00002-of-00002.gguf

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README.md

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+---
+license: other
+license_name: qwen-community-license-1.0
+base_model: Qwen/Qwen3.8-Flash-Next
+tags:
+- llama.cpp
+- moe
+- pruning
+- reap
+- qwen
+pipeline_tag: text-generation
+---
+
+# Qwen3.8-Flash-Next REAP-320 "multi-domain" — GGUF
+
+The successor to [REAP-256 "duo"](https://huggingface.co/AnonimousA/Qwen3.8-Flash-Next-REAP-256-duo-GGUF): **512 → 320 experts per layer, 83.8 → 64.2 GiB**, calibrated on a **multi-domain corpus** instead of code-only. Built directly in response to [this community critique](https://huggingface.co/AnonimousA/Qwen3.8-Flash-Next-REAP-256-duo-GGUF/discussions/2) — the full head-to-head against Unsloth's UD-IQ1_M, including where this build loses, is published in that thread.
+
+**Not an official Qwen release.** Community derivative, unaffiliated with Alibaba/Qwen.
+
+## Why K=320 and why multi-domain
+
+- **K=320 sits above the reliability knee.** Two independent measurements (sh0wie's REAP ladder, and our own K=256 lineage) show rare-token reliability and clean termination degrading below ~K=288-320. At K=320: zero empty-content stalls and zero truncation across all our battery passes, 9/10 on the seeded rare-name probe.
+- **Calibration is the product.** Salience was measured with `llama-imatrix` over a 525K-token corpus: 30% agentic / 30% code / 15% conversation / 12.5% math / 12.5% writing. Against our real multi-domain traffic, this selection retains 82.7% of routing mass vs 75.3% for a code-only selection at the same K. The multi-domain build beat the code-only specialist **even at code** (HumanEval 96.3% vs 95.7%).
+
+## Measured (all on RTX 5090 32 GB, llama.cpp + expert-cache PR #27861 build)
+
+| metric | this build | unpruned baseline |
+|---|---|---|
+| 6-task machine-verified battery (3 passes, effort medium) | **13.0 / 13.0 / 12.0** | 12 / 13 / 11 (UD-Q2_K_XL) |
+| HumanEval-164 pass@1 (seed 42, effort low) | **96.3%** (158/164) | 95.7% (UD-IQ1_M) |
+| Rare-name probe (10 seeded gens) | 9/10 | 8/10 (Q2), 5/10 (IQ1_M, truncation) |
+| General-knowledge fabrication (12 questions) | **25%** | **0% (UD-IQ1_M)** |
+| Decode with LRU-128 expert cache, 32 GB card | ~42-45 tok/s | ~42-46 (Q2 full, same cache) |
+| Prefill, same regime | **337 tok/s (+23%)** | 273 tok/s |
+
+**Honest limitation, stated loudly:** pruning experts costs world knowledge. On our 12-question fabrication probe this build fabricates 25% (the old K=256 duo: 42%; unpruned UD-IQ1_M: 0%). If you want a general-purpose assistant, take [Unsloth's UD-IQ1_M](https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF) — experts store knowledge, bits store precision. Take this build when **fit and speed in constrained VRAM** dominate: it is 5 GiB smaller than the 1-bit route, prefills ~23% faster under the same cache regime, and generates concisely.
+
+## Files
+
+Single quant: `UD-Q3_K_XL`-lineage (pruned by binary copy from [Unsloth's quant](https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF) — surviving experts keep their original bits, no requantization). 2 shards, 64.2 GiB total.
+
+`manifests/seleccion_mass_K320.json` ships the per-layer kept-expert manifest (`{"<layer>": [expert ids...]}`, stock ids 0-511) — the prune is reproducible from the source quant, and the manifest is directly usable by SSD-expert-streaming runtimes as a pinned set.
+
+## Run it
+
+Needs llama.cpp mainline ≥ 2026-08-27 (PR #27742, `qwen4exp` support):
+
+```bash
+llama-server -m Qwen3.8-Flash-Next-UD-Q3_K_XL-reap320-00001-of-00002.gguf \
+  -ngl 99 --cpu-moe -c 32768 -fa on --jinja --parallel 1 \
+  --temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.0
+```
+
+On a 32 GB card, add the expert-cache from [PR #27861](https://github.com/ggml-org/llama.cpp/pull/27861) (`--moe-expert-cache 128`) for ~42-45 tok/s decode. Set reasoning effort explicitly (`"chat_template_kwargs": {"reasoning_effort": "low"}`) — the template defaults to `xhigh` and replies take minutes.
+
+## Traps (measured, not theoretical)
+
+1. `--parallel > 1` requires `--kv-unified`.
+2. Empty `content` with tight `max_tokens`: reasoning burns the budget. Detect and retry with a higher cap.
+3. First requests after a cold load pay disk; throughput climbs over ~3-4 requests to its warm ceiling.
+
+## Credits
+
+- [Qwen](https://huggingface.co/Qwen) for Qwen3.8-Flash-Next (Qwen Community License 1.0).
+- [Unsloth](https://huggingface.co/unsloth) for the UD-Q3_K_XL quant this was pruned from, and for UD-IQ1_M, the honest benchmark opponent.
+- [Cerebras Research](https://github.com/CerebrasResearch/reap) for the REAP method.
+- [sh0wie](https://huggingface.co/sh0wie) for independently mapping the pruning-reliability knee and publishing saliency manifests.
+- The critic in [discussion #2](https://huggingface.co/AnonimousA/Qwen3.8-Flash-Next-REAP-256-duo-GGUF/discussions/2), whose pushback made this model exist.

manifests/seleccion_mass_K320.json

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