---

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.

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)

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 FAST GGUF — AMD Ryzen AI Max+ 395 / gfx1151

⚠️ 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.

⛔ ngram-mod at ≥64K can wedge the GPU

--spec-type ngram-mod is the right spec-decode for this family on ROCm, and it is fine at 32K. At 64K and above, a context-checkpoint restore desyncs the QSA indexer cache from the KV caches. The next kernel then wedges the SDMA queue — Ring sdma0 reset failure, D-state llama-server, kill -9 useless, hard power-off to recover. Field report by @liusecret on Qwen3.8-Flash-Next-ROCmFP4-STRIX-GGUF#6.

Workaround (no new binary required):

-ctxcp 0 -cpent -1

--ctx-checkpoints 0 plus --checkpoint-every-n-tokens -1. If no checkpoints are created, the restore path never runs. Keep ngram-mod on ROCm; do not use it on Vulkan/gfx1151 (acceptance collapses to 0). A fork that serialises mem_idx is the real fix — until that binary is in your hands, use the flags.

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.

FAST is the smallest tier and the one to take if you are disk-constrained: the Q4_0_ROCMFP4_FAST recipe — attention, experts, token embeddings and the PLE table all ROCmFP4, with only the output head lifted to Q6_K. Converted to BF16 GGUF and quantized by me from their release. 4.27 bpw, 87.94 GiB.

tensor group	type
MoE expert weights (ffn_*_exps)	TYPE_101 (ROCmFP4, 4.251 bpw)
shared expert (ffn_*_shexp)	TYPE_101
attention (attn_*)	all TYPE_101
per_layer_token_embd.weight (PLE, 51.2B params)	TYPE_101
token_embd.weight	TYPE_101
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
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: 22.75 tok/s
• prompt processing: 387.3 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-FAST-00001-of-00003.gguf	41.63 GiB
Qwen3.8-Flash-Next-Uncensored-Q4_0-ROCmFP4-FAST-00002-of-00003.gguf	41.60 GiB
Qwen3.8-Flash-Next-Uncensored-Q4_0-ROCmFP4-FAST-00003-of-00003.gguf	4.71 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-FAST-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.

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.