readme: clean public version
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Julian Beltran
@@ -5,12 +5,10 @@
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**qwen3.8-flash-next on amd strix halo — 91g quant, 56 tok/s, 262k context**
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[](https://huggingface.co/julianmb/Qwen3.8-Flash-Next-IQ4_XS-GGUF)
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[](https://github.com/Nathanw1014/llama.cpp/tree/strix-halo-vulkan)
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[](https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/main/LICENSE)
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[](#results)
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[](#results)
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[](#the-converter-bug)
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[](#docker)
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*every published quant byte-traced back to the official checkpoint*
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@@ -20,9 +18,9 @@
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## results
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engine: [nathanw1014/llama.cpp `strix-halo-vulkan`](https://github.com/Nathanw1014/llama.cpp/tree/strix-halo-vulkan) · vulkan/radv · q8_0 kv · `-ub 2048` · temp 0
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91g quant · vulkan/radv · mtp sidecar · q8_0 kv · `-ub 2048` · temp 0 · 128g strix halo
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| depth | plain pp/tg | mtp pp/tg |
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| depth | plain pp / tg | mtp pp / tg |
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|------:|:-----------:|:---------:|
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| 0 | 92.5 / 29.9 | 87.0 / **53.1** |
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| 8k | 480 / 24.1 | 458 / **56.4** |
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@@ -54,7 +52,7 @@ single runs, n=1 caveat. pick your file by use case — see the table above.
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|------|------|:------------:|
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| `...-IQ4_XS-`**`PLE`**`.gguf` | 91 giB | ctx ≤ 32k — wins everywhere, mtp to 56 t/s |
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| `...-IQ4_XS.gguf` | 116 giB | ctx ≥ 128k — faster mtp at depth, wider fork compat |
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| `mtp-...-Q8_0.gguf` | 3.9 giB | mtp sidecar for nathanw1014-lineage engines |
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| `mtp-...-Q8_0.gguf` | 3.9 giB | mtp sidecar, required for the speed numbers |
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<details>
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<summary><b>the PLE cut — why the 51b n-gram table tolerates 4-bit</b></summary>
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@@ -66,18 +64,18 @@ depth sweep. the cut: `--tensor-type "per_layer_token_embd=IQ4_XS"` on our
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quantizer → 54g → 27g.
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**fork caveat:** engines that feed gathered PLE rows straight into mul_mat as
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quantized B operands assert (apepojken-class, ggml-vulkan.cpp:7794). verified
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working on nathanw1014 strix-halo-vulkan and rocmfpx.
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quantized B operands assert (ggml-vulkan.cpp:7794). verified working on the
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packaged engine and rocmfpx.
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</details>
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### pick your setup
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| your use case | quant | engine | ctx | expect |
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|---|---|---|---|---|
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| coding agents, chat | 91g PLE | [nathanw1014 vulkan](https://github.com/Nathanw1014/llama.cpp/tree/strix-halo-vulkan) + mtp | ≤ 32k | 56 t/s |
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| long documents | 116g static | same engine + mtp | 128k | 27 t/s |
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| full rag / research | 116g static | [rocm 10 container](https://github.com/MorezMartin/engramhalo-rocm10) + ssd streaming | 262k | 14 t/s |
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| your use case | quant | ctx | expect |
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|---|---|---|---|
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| coding agents, chat | 91g PLE | ≤ 32k | 56 t/s |
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| long documents | 116g static | 128k | 27 t/s |
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| full rag / research | 116g static + ssd streaming | 262k | 14 t/s |
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---
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@@ -96,8 +94,9 @@ our first quant printed deterministic garbage at temp 0. bisect to root cause:
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> [!WARNING]
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> **any fork rolling its own qwen4exp converter must fold `(1 + w)` into the
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> hyper-connection gammas.** upstream runtime documents the contract at
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> `qwen4exp.cpp:231`. miss it and every layer normalizes wrong — garbage
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> from layer 0, all shapes correct, all shape-only tests pass.
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> `qwen4exp.cpp:231 — "the converter folded each gamma to (1 + w)"`. miss it
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> and every layer normalizes wrong — garbage from layer 0, all shapes correct,
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> all shape-only tests pass.
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fix + regression test: rocmfpx `port-qwen4exp` commit `61b6a3b48`
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([pr charlie12345/ROCmFPX#98](https://github.com/charlie12345/ROCmFPX/pull/98))
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@@ -112,7 +111,11 @@ docker compose up --build
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# serve on :8080 — vulkan/radv, no rocm install needed
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```
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add the mtp sidecar for 56 t/s:
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the packaged engine is tuned for strix halo: vulkan fa/mmq kernels, graph
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reuse, lazy ple streaming, quantized-kv attention — the combination behind
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the 56 t/s numbers. no manual build, no host rocm install.
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add the mtp sidecar for speculative decoding:
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```bash
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docker compose run qwen38-flash-next /app/llama-server \
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@@ -120,19 +123,27 @@ docker compose run qwen38-flash-next /app/llama-server \
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--spec-type draft-mtp --spec-draft-n-max 6 --spec-draft-p-min 0.75
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```
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### 262k context (ssd streaming)
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swap the model to the static 116g and enable lazy ple — the n-gram table
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stays on ssd (~2.5g resident), leaving room for the full context window:
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```bash
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docker compose run qwen38-flash-next /app/llama-server \
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-m /models/Qwen3.8-Flash-Next-IQ4_XS.gguf \
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-c 262144 -lm mmap --tensor-read-lazy on \
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-ngl 999 -fa on -ctk q8_0 -ctv q8_0 -ub 2048 -t 4
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```
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---
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## 🔬 engine merge (paused)
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## 🔬 the n-gram table at 4-bit — what we found
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we merged nathanw1014's branch (175 commits: vulkan perf stack, qwen4exp
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runtime past the squash, lazy ple, spec-decode fixes) into ggml-org master —
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one engine with lazy ple (262k on 66g resident) + depth fixes + master's
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general improvements. paused at 53% build: the branches diverged semantically
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in shared enum/base-class files.
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- [`docs/engine-cherry-pick-plan.md`](docs/engine-cherry-pick-plan.md) — full
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175-commit classification
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- [`docs/engine-merge-status.md`](docs/engine-merge-status.md) — resume point
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the 51b ple lookup table tolerates iq4_nl (4.25 bpw) with no quality loss
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across the depth sweep. but there's a depth-dependent reversal: under mtp at
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128k+, the ple quant *loses* to the static quant (18.6 vs 26.9 t/s) — the
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iq4_nl noise compounds over deep n-gram history and lowers draft acceptance.
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n=1, single runs. pick your file by use case.
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---
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@@ -140,11 +151,10 @@ in shared enum/base-class files.
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| path | what |
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|------|------|
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| `models/` | symlink farm to `/mnt/ssd2/models/` (never in git) |
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| `results/` | dated receipts, one per investigation |
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| `scripts/` | conversion pipeline, oracle, depth bench, resume helpers |
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| `reddit/` | archived community threads that drove the investigation |
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| `models/` | symlink farm to local ssd (never in git) |
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| `docs/` | engine merge plan, cherry-pick classification |
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| `Dockerfile` | two-stage: vulkan engine build + slim runtime |
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| `docker-compose.yml` | one-liner serving with recommended flags |
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---
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