---

license: other

license_name: qwen-community-1.0

base_model:

- orcarouter/Qwen3.8-Flash-Next-Uncensored

- Qwen/Qwen3.8-Flash-Next

base_model_relation: quantized

pipeline_tag: text-generation

library_name: gguf

tags:

- gguf

- rocmfp4

- llama.cpp

- strix-halo

- gfx1151

- rocm

- amd

- ryzen-ai-max

- uncensored

- research

---

> ### 🔧 Runtime: build the ROCmFPX fork below

> Stock llama.cpp will not load this file. You need both the qwen4exp architecture

> and the ROCmFP4 tensor types in one tree. Upstream

> charlie12345/ROCmFPX has the ROCmFP4 types but

> not qwen4exp. Our fork has both:

>

> kingjones30/ROCmFPX — a fork of charlie12345/ROCmFPX, branch main.

>

> ```bash

> git clone https://github.com/kingjones30/ROCmFPX.git

> cd ROCmFPX

> cmake -B build -DGGML_HIP=ON -DGPU_TARGETS=gfx1151 -DGGML_NATIVE=ON -DCMAKE_BUILD_TYPE=Release

> cmake --build build --target llama-server llama-quantize -j$(nproc)

> ```

>

> ⚠️ Apply the bundled fix patches before cmake: qwen4exp-qsa-checkpoint-fix.patch

> always, plus qwen4exp-mtp-graph-fork.patch if you want --spec-type draft-mtp on this

> clone. Full steps further down.

>

> Verified 2026-08-27 on gfx1151: clean clone → 0 build errors → llama-server loads a

> qwen4exp ROCmFP4 GGUF from this family and generates coherent text.

Qwen3.8-Flash-Next-Uncensored — ROCmFP4 STRIX\_LEAN GGUF — AMD Ryzen AI Max+ 395 / gfx1151

⚡ Speculative decoding (MTP) now works — measured +27.7% at short context

The qwen4exp MTP graph shipped with a broken combiner (it mean-pooled the hyper-connection

streams), so --spec-type draft-mtp acceptance sat near 0.36 and gave no real speedup. That is

now fixed — this repo ships qwen4exp-mtp-graph.patch; apply it to the tree the build steps below produce and rebuild

(git apply qwen4exp-mtp-graph.patch before cmake --build).

Pair the model with a Flash-Next MTP head from

kingjones777/Qwen3.8-Flash-Next-MTP-Heads-GGUF.

Measured on the Uncensored FAST (imatrix) build with the Q8_0 head (mtp-Qwen3.8-Flash-Next-Q8_0.gguf) at

short context (-c 2048): acceptance 0.94, 31.80 tok/s vs 24.9 tok/s no-draft

(+27.7%), warm 160-token completion, cache_prompt:false. The graph fix and the heads are shared

across the Flash-Next family, but this tier's own MTP speed has not been measured, and the Q6_K / Q4

heads were not benchmarked. The head only proposes draft tokens; the main model verifies every one,

so your output is unchanged.

llama-server -m <the first shard in this repo>.gguf \
  -md mtp-Qwen3.8-Flash-Next-Q8_0.gguf --spec-type draft-mtp \
  --spec-draft-n-min 0 --spec-draft-n-max 1 --n-gpu-layers-draft 99 \
  -ngl 999 -fa on -np 1 -c 32768 --jinja

-np 1 is required with draft-mtp.

⚠️ Updated 2026-09-17 — re-download if you pulled it earlier. qwen4exp-mtp-graph.patch now

carries the models.h and llama-model.cpp hunks it needs. The previous version applied cleanly but

failed to compile ('graph_mtp' was not declared in this scope). The bundled patch matches the

build steps on this card; for the other build path use qwen4exp-mtp-graph-fork.patch (if you build from a kingjones30/ROCmFPX clone), also bundled here.

Measured plain vs draft-mtp — median of 3 per cell, one binary, greedy, cache_prompt:false,

256 generated tokens, -c 2048, Q8_0 head, Uncensored STRIX_LEAN-imatrix weights, gfx1151 / ROCm 7.2.4

(2026-09-17):

| workload | plain | --spec-draft-n-max 4 | --spec-draft-n-max 1 |

|---|---|---|---|

| reasoning | 23.91 | 30.94 (+29%, acc 0.680) | 31.94 (+34%, acc 0.945) |

| JSON output | 23.99 | 28.31 (+18%, acc 0.597) | 27.24 (+14%, acc 0.758) |

| code | 24.09 | 21.56 (−10%, acc 0.422) | 26.80 (+11%, acc 0.711) |

| long-document summary | 23.80 | 20.36 (−14%, acc 0.352) | 24.14 (+1%, acc 0.641) |

⭐ Use --spec-draft-n-max 1. It did not lose a single workload here, and it wins most where the

next token is predictable. n-max 4 pays for four draft forward passes per step, so it only wins when

acceptance is high (reasoning, JSON) and is a genuine loss on code and long-document work. MTP also

costs prefill speed, because the draft head processes the prompt too. The older +27.7% figure came

from one reasoning-shaped prompt — it holds for that shape, not universally, so measure your own.

> ✅ Depth: with the bundled checkpoint fix applied, draft-mtp is verified from 2K to

> 128K — see the box further down for what was measured and what is still open.

⚠️ Research artifact. Refusal behaviour has been removed. This does not add capability — it

removes guardrails. Use it deliberately, in a context where that is appropriate, and own the output.

> ### ✅ Depth: draft-mtp is fixed and measured (2026-09-17)

>

> The ≥64K wedge came from context-checkpoint restores leaving the QSA indexer cache (mem_idx) out

> of the checkpoint. The fix ships here as

> qwen4exp-qsa-checkpoint-fix.patch — it overrides

> state_write / state_read on llama_memory_hybrid_idx. Apply it with the build steps on this

> card even if you never use speculative decoding.

>

> With it applied, --spec-type draft-mtp ran clean from 2K to 128K on gfx1151: 8 depth rungs,

> 864 context-checkpoint restores (2 of them prompt-cache rollbacks at 64K), 0 GPU faults,

> coherent output at every depth. Measured 2026-09-17 on Ryzen AI MAX+ 395 / ROCm 7.2.4 with the

> Uncensored STRIX_LEAN-imatrix weights + mtp-Qwen3.8-Flash-Next-Q8_0.gguf, -c 262144,

> --spec-draft-n-max 4, default context checkpoints. That 128K run used my own fork tree; the exact

> build steps on this card were verified to 16K.

>

> ⚠️ Still open: --spec-type ngram-mod at ≥64K has not been retested with the patch — the

> original field report (…-STRIX-GGUF#6, thanks

> @liusecret) was ngram-mod, so keep -ctxcp 0 -cpent -1 when you

> use it. And do not use speculative decoding of any kind on Vulkan/gfx1151 — acceptance collapses to 0.

>

> A speculative replay stalled warning on ~2% of restores is expected and harmless: that is the

> server's livelock guard dropping one draft and decoding that token normally.

Quantized from the BF16 weights published by

orcarouter/Qwen3.8-Flash-Next-Uncensored

— the abliteration work here is theirs, not mine. Go star their repo.

STRIX\_LEAN is my size/speed tier for Strix Halo: the Q4_0_ROCMFP4_STRIX_LEAN recipe — ROCmFP4

weights with Strix attention K/V handling, Q5_K token embeddings, and a Q6_K output head.

Converted to BF16 GGUF and quantized by me from their release. 4.78 bpw, 98.49 GiB.

| tensor group | type |

|---|---|

| MoE expert weights (ffn_*_exps) | TYPE_101 (ROCmFP4, 4.251 bpw) |

| shared expert (ffn_*_shexp) | TYPE_101 |

| attention (attn_*) | half TYPE_100, half TYPE_101 |

| per_layer_token_embd.weight (PLE, 51.2B params) | Q5_1 |

| token_embd.weight | Q5_K |

| output.weight (lm head) | Q6_K |

The size matches my aligned build of the same tier to 0.01 GiB — the abliterated checkpoint is

structurally identical, so the quant recipe transfers exactly.

The Q6_K head

output.weight is Q6_K, never 4-bit. Every sampled token passes through the lm head, so its

quantization error lands directly in the argmax. Verified by exact tensor name after both

quantize and split — output.weight is a substring of attn_output.weight, so a loose check

reports success on a 4-bit head.

Building a runtime that loads these files

Needs two things in one tree: the qwen4exp architecture and the ROCmFP4 tensor types.

charlie12345/ROCmFPX has the ROCmFP4 types but not qwen4exp; the upstream qwen4exp work has no

ROCmFP4. The patch combining them ships in this repo:

qwen4exp-on-rocmfpx-d3ca537.patch (156 KB, 25 files).

git clone https://github.com/charlie12345/ROCmFPX.git
cd ROCmFPX && git checkout d3ca537
curl -LO https://huggingface.co/kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-STRIX_LEAN-GGUF/resolve/main/qwen4exp-on-rocmfpx-d3ca537.patch
git apply qwen4exp-on-rocmfpx-d3ca537.patch
# both fixes ship in this repo — apply them before configuring:
curl -LO https://huggingface.co/kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-STRIX_LEAN-GGUF/resolve/main/qwen4exp-qsa-checkpoint-fix.patch
git apply qwen4exp-qsa-checkpoint-fix.patch      # checkpoint safety at >=64K: apply this always
curl -LO https://huggingface.co/kingjones777/Qwen3.8-Flash-Next-Uncensored-ROCmFP4-STRIX_LEAN-GGUF/resolve/main/qwen4exp-mtp-graph.patch
git apply qwen4exp-mtp-graph.patch               # only if you want --spec-type draft-mtp
cmake -B build -DGGML_HIP=ON -DGPU_TARGETS=gfx1151 -DGGML_NATIVE=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build --target llama-server llama-quantize -j$(nproc)

Verified from a clean clone: applies without conflicts, compiles with zero errors, and the built

llama-server loads these GGUFs and generates. The patch's new files —

src/llama-memory-hybrid-idx.{cpp,h} (the QSA indexer's own memory class),

src/models/qwen4exp.cpp, conversion/qwen4exp.py — are the pieces hand-copying misses.

Measured — Ryzen AI MAX+ 395, gfx1151, ROCm 7.2.4, full 49/49 offload

• generation: 23.20 tok/s
• prompt processing: 377.8 tok/s
• GPU memory: 63.3 GiB resident — identical to the aligned build

GPU-only, full offload. I do not publish partial-offload speeds.

*Measured with one fixed 6,963-token prompt reused across samples (cache_prompt: false), run 1

discarded as warm-up, median of the 4 settled samples — spread 1.6 tok/s. An earlier figure of

222 tok/s came from a flawed method that used a different corpus slice per sample; that injected

slice-to-slice variance straight into the number. Same file, same GTT (63.6 GiB) — only the

measurement changed.*

Long context

This model's native max is 262,144, and it runs there on a 128 GB box:

| context | prompt | pp tok/s | gen tok/s | GTT |

|---|---|---|---|---|

| 131,072 | 111,411 | 196 | 15.22 | 69.1 GiB |

| 262,144 | 8,000 | 307 | 22.48 | 72.0 GiB |

| 262,144 | 200,000 | 128 | 10.46 | 74.9 GiB |

The context window is nearly free — GTT grows only ~4 GiB from 8k to 128k, because Qwen Sparse

Attention caps KV. What you pay for is depth: a 200k-token prompt halves generation. It

degrades smoothly rather than falling off a cliff.

Refusal / quality (counts only)

Aligned build vs this one, same prompts, greedy, same harness:

| split | aligned | this build |

|---|---|---|

| Harmful (24) | 0 comply | 22 comply |

| Harmless (12) | 10 ok | 11 ok |

| Quality (8) | 6/8 | 6/8 — same two failures |

Quality is unchanged to the specific failing question, which is the point: the abliteration

flipped refusal without the quant damaging the model. Prompts and completions are not published.

Files

Sharded to stay under HF's 50 GB limit. Point --model at the first shard.

| file | size |

|---|---|

| Qwen3.8-Flash-Next-Uncensored-Q4_0-ROCmFP4-STRIX_LEAN-00001-of-00003.gguf | 41.86 GiB |

| Qwen3.8-Flash-Next-Uncensored-Q4_0-ROCmFP4-STRIX_LEAN-00002-of-00003.gguf | 41.62 GiB |

| Qwen3.8-Flash-Next-Uncensored-Q4_0-ROCmFP4-STRIX_LEAN-00003-of-00003.gguf | 15.01 GiB |

| mmproj-Qwen3.8-Flash-Next-Uncensored-BF16.gguf | 0.85 GiB (vision tower) |

Usage

llama-server \
  --model Qwen3.8-Flash-Next-Uncensored-Q4_0-ROCmFP4-STRIX_LEAN-00001-of-00003.gguf \
  --mmproj mmproj-Qwen3.8-Flash-Next-Uncensored-BF16.gguf \
  --host 127.0.0.1 --port 8080 \
  --n-gpu-layers 999 --flash-attn on --fit off \
  --ctx-size 131072 --threads 16 --jinja

Do not use --no-mmap. The PLE table is streamed from the file through the page cache; forcing

it into anonymous memory gets the process OOM-killed with nothing in the server log.

<!-- CREDITS:START -->

Acknowledgements

charlie12345/ROCmFPX — defines the ROCmFP4 tensor

formats. Every file here was produced with its llama-quantize and runs on its runtime. MIT, based

on upstream llama.cpp. The qwen4exp architecture is not part of that fork — it comes from

upstream llama.cpp work and is applied on top via

qwen4exp-on-rocmfpx-d3ca537.patch in this repo.

llama.cpp — ggml-org and contributors — the engine,

GGUF format and conversion tooling this is built on.

AMD ROCm — the compute platform targeted here (ROCm 7.2.4, gfx1151).

orcarouter — published the uncensored BF16 checkpoint

this is built from. The abliteration is their engineering; I only converted and quantized it.

Qwen team — the original base model. See base_model; license qwen-community-1.0.

<!-- CREDITS:END -->