What license applies to plunderstruck/Qwen3.6-27B-OBLITERATED-MTP-ROCmFP4-GGUF?

License: apache-2.0. Verify terms on Hugging Face before commercial use.

Model Intelligence Sheet

plunderstruck/Qwen3.6-27B-OBLITERATED-MTP-ROCmFP4-GGUF overview

Q: How do I run plunderstruck/Qwen3.6-27B-OBLITERATED-MTP-ROCmFP4-GGUF locally?

Download a GGUF file from this page and load it in guIDE or llama.cpp. Pipeline task: text-generation.

Q: How much VRAM or disk space does plunderstruck/Qwen3.6-27B-OBLITERATED-MTP-ROCmFP4-GGUF need?

Runs locally from ~1.72 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

<div style="border:2px solid currentColor; font family:ui monospace,'SF Mono','Cascadia Mono',Consolas,'Liberation Mono',monospace;" <div style="border bottom:…

ggufrocmfp4qwen3.6obliteratedabliterateduncensored27bmtpspeculative-decodingstrix-haloamdrocmvulkanenbase_model:OBLITERATUS/Qwen3.6-27B-OBLITERATEDbase_model:quantized:OBLITERATUS/Qwen3.6-27B-OBLITERATEDlicense:apache-2.0region:us

Runs locally from ~1.72 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline

—

Author

plunderstruck

Repository Files & Downloads

2 GGUF files detected

Direct downloads for local inference

File	Type	Quantization	Size	Link
Qwen3.6-27B-OBLITERATED-MTP-ROCmFP4-STRIX-embF16-imatrix-headQ6.gguf	GGUF	GGUF	15.70 GB	Download
mmproj-F32.gguf	GGUF	F32	1.72 GB	Download

Model Details

Model ID	plunderstruck/Qwen3.6-27B-OBLITERATED-MTP-ROCmFP4-GGUF
Author	plunderstruck
Pipeline	—
License	apache-2.0
Base model	OBLITERATUS/Qwen3.6-27B-OBLITERATED
Last modified	2026-06-21T04:43:35.000Z

Model README

---

base_model: OBLITERATUS/Qwen3.6-27B-OBLITERATED

base_model_relation: quantized

license: apache-2.0

library_name: gguf

tags:

gguf
rocmfp4
qwen3.6
obliterated
abliterated
uncensored
27b
mtp
speculative-decoding
strix-halo
amd
rocm
vulkan

language:

---

<div style="border-bottom:1px solid currentColor; padding:6px 12px; font-size:11px; letter-spacing:3px; text-transform:uppercase; opacity:0.7; text-align:center;">PLUNDERSTRUCK // ROCmFP4 QUANTIZED MODEL // STRIX HALO · gfx1151</div>

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</pre>

<div style="font-size:23px; font-weight:800; letter-spacing:1px;">QWEN3.6-27B-OBLITERATED-MTP</div>

<div style="font-size:12.5px; letter-spacing:1px; opacity:0.8; margin-top:5px;"><span style="white-space:nowrap;">4-BIT ROCmFP4</span> · <span style="white-space:nowrap;">ABLITERATED / UNCENSORED</span> · <span style="white-space:nowrap;">GRAFTED MTP SELF-SPECULATIVE DECODE</span> · <span style="white-space:nowrap;">VISION-CAPABLE</span> · <span style="white-space:nowrap;">SINGLE AMD APU</span></div>

</div>

<tr>

<td style="border-top:1px solid currentColor; border-right:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">FORMAT</div><div style="font-weight:700;">ROCmFP4 4-BIT</div></td>

<td style="border-top:1px solid currentColor; border-right:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">PRECISION</div><div style="font-weight:700;">~4.8 BPW</div></td>

<td style="border-top:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">CONTEXT</div><div style="font-weight:700;">262 K</div></td>

</tr>

<tr>

<td style="border-top:1px solid currentColor; border-right:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">DRAFT</div><div style="font-weight:700;">MTP n-max 5 (GRAFTED)</div></td>

<td style="border-top:1px solid currentColor; border-right:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">VISION</div><div style="font-weight:700;">QWEN3-VL</div></td>

<td style="border-top:1px solid currentColor; border-right:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">BACKEND</div><div style="font-weight:700;">VULKAN0</div></td>

<td style="border-top:1px solid currentColor; padding:8px 12px;"><div style="font-size:10px; letter-spacing:1px; opacity:0.6;">LICENSE</div><div style="font-weight:700;">APACHE-2.0</div></td>

</tr>

</table>

</div>

<b style="color:#dc2626; letter-spacing:1px;">⚠ REQUIRES THE ROCmFP4 FORK</b><br>

The custom <code>q4_0_rocmfp4</code> / <code>q4_0_rocmfp4_fast</code> tensor types <b>will not load in stock llama.cpp, LM Studio, or Ollama</b>. Build/run with <a href="https://github.com/charlie12345/ROCmFPX">charlie12345/ROCmFPX</a> · branch <code>mtp-rocmfp4-strix</code>.

</div>

<b>NOTE //</b> Ignore HuggingFace's auto-detected "F16"/16-bit badge — its parser can't read ROCmFP4 and mislabels by the f16 embeddings. These are <b>~4.8 bpw 4-bit</b> files; pick by filename.

</div>

<b>NOTE //</b> <b>Uncensored:</b> this is an <i>abliterated</i> model — the refusal direction was removed (with source-weight interpolation to retain capability) upstream by OBLITERATUS. It will answer prompts a stock Qwen3.6 would refuse. Abliteration is upstream work; verify behavior before relying on it.

</div>

<div style="font-family:ui-monospace,'SF Mono',Consolas,monospace; font-weight:800; font-size:14px; letter-spacing:2px; text-transform:uppercase; border-bottom:2px solid currentColor; padding-bottom:5px; margin:26px 0 12px;"><span style="color:#ea580c;">01</span> · FILES</div>

<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Output head</th>

</tr></thead>

<tbody>

<tr><td style="border:1px solid currentColor; padding:7px 10px;"><code>…-STRIX-embF16-imatrix-headQ6.gguf</code> ★</td><td style="border:1px solid currentColor; padding:7px 10px;">~15.5 GB</td><td style="border:1px solid currentColor; padding:7px 10px;">Q6_K</td><td style="border:1px solid currentColor; padding:7px 10px;"><b>the one build</b> — best speed/quality balance: f16 embeddings + Q6 output head on the fast single-scale body</td></tr>

</tbody>

</table>

</div>

One file — the best speed/quality balance in ROCmFP4 for Strix Halo. It keeps the two quality levers that are actually felt — genuine f16 token embeddings (from BF16) and a Q6_K output head — on the fast single-scale q4_0_rocmfp4_fast body + a general+code-calibrated imatrix + the grafted MTP draft head. Repo also bundles the mmproj-F32.gguf Qwen3-VL vision projector and chat_template.jinja (froggeric's unified Qwen3.6 template — tool calls + inline <|think_off|>/<|think_on|> + vision).

Run from the folder holding the .gguf + chat_template.jinja:

env HSA_OVERRIDE_GFX_VERSION=11.5.1 GGML_HIP_ENABLE_UNIFIED_MEMORY=1 \
llama-server \
  -m Qwen3.6-27B-OBLITERATED-MTP-ROCmFP4-STRIX-embF16-imatrix-headQ6.gguf \
  --alias obliterated-27b-mtp \
  --host 0.0.0.0 \
  --port 8080 \
  -dev Vulkan0 \
  -ngl 999 \
  -fa on \
  -c 262144 \
  -b 2048 \
  -ub 256 \
  -t 16 \
  -tb 16 \
  -ctk f16 \
  -ctv f16 \
  -cpent 256 \
  -ctxcp 32 \
  --cache-reuse 256 \
  --cache-ram 65536 \
  --temp 0.6 \
  --top-p 0.95 \
  --top-k 20 \
  --min-p 0.0 \
  --spec-type draft-mtp \
  --spec-draft-device Vulkan0 \
  --spec-draft-ngl all \
  --spec-draft-type-k f16 \
  --spec-draft-type-v f16 \
  --spec-draft-n-max 5 \
  --spec-draft-n-min 0 \
  --spec-draft-p-min 0.0 \
  --spec-draft-p-split 0.10 \
  --chat-template-file chat_template.jinja \
  --reasoning on \
  --reasoning-format deepseek \
  --chat-template-kwargs '{"preserve_thinking": true}' \
  --jinja \
  --parallel 1 \
  --metrics \
  --no-mmap \
  --mmproj mmproj-F32.gguf \
  --image-min-tokens 1024

The last two lines enable vision — the mmproj-F32.gguf Qwen3-VL projector is bundled in this repo (projection_dim 5120); omit them for text-only. --image-min-tokens 1024 is required whenever --mmproj is set (see §03).

<th style="border:1px solid currentColor; padding:6px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Function</th>

</tr></thead>

<tbody>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>HSA_OVERRIDE_GFX_VERSION=11.5.1</code></td><td style="border:1px solid currentColor; padding:6px 10px;">treat the APU as gfx1151 (Strix Halo)</td></tr>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>GGML_HIP_ENABLE_UNIFIED_MEMORY=1</code></td><td style="border:1px solid currentColor; padding:6px 10px;">allow use of the full 128 GB unified memory</td></tr>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>-dev Vulkan0</code></td><td style="border:1px solid currentColor; padding:6px 10px;">run on Vulkan — fastest backend for ROCmFP4 on Strix Halo</td></tr>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>-ngl 999 · -fa on</code></td><td style="border:1px solid currentColor; padding:6px 10px;">offload all layers · flash attention</td></tr>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>-c 262144</code></td><td style="border:1px solid currentColor; padding:6px 10px;">context length (256K)</td></tr>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>-b 2048 · -ub 256 · -t/-tb 16</code></td><td style="border:1px solid currentColor; padding:6px 10px;">prefill batch / micro-batch · CPU threads</td></tr>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>-ctk f16 · -ctv f16</code></td><td style="border:1px solid currentColor; padding:6px 10px;">f16 KV cache — how we run it; drop to <code>q8_0</code>/<code>q4_0</code> to use less memory</td></tr>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>-cpent · -ctxcp · --cache-reuse · --cache-ram 65536</code></td><td style="border:1px solid currentColor; padding:6px 10px;">cross-turn KV checkpointing + 64 GB resident reuse cache</td></tr>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>--temp 0.6 --top-p 0.95 --top-k 20 --min-p 0.0</code></td><td style="border:1px solid currentColor; padding:6px 10px;">Qwen3.6 recommended sampling</td></tr>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>--spec-type draft-mtp · --spec-draft-n-max 5</code></td><td style="border:1px solid currentColor; padding:6px 10px;">grafted MTP head, self-speculative; draft depth 5</td></tr>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>--spec-draft-device Vulkan0 · -ngl all · type-k/v f16</code></td><td style="border:1px solid currentColor; padding:6px 10px;">draft head on Vulkan, fully offloaded, f16 KV (matches the main model)</td></tr>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>--chat-template-file chat_template.jinja</code></td><td style="border:1px solid currentColor; padding:6px 10px;">bundled froggeric template (tool calls + think-toggle + vision)</td></tr>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>--reasoning on --reasoning-format deepseek + kwargs {preserve_thinking:true}</code></td><td style="border:1px solid currentColor; padding:6px 10px;">thinking enabled, deepseek-style parsing; keep cross-turn cache</td></tr>

<tr><td style="border:1px solid currentColor; padding:6px 10px;"><code>--jinja --parallel 1 --metrics --no-mmap</code></td><td style="border:1px solid currentColor; padding:6px 10px;">apply template · single slot · metrics · weights in RAM</td></tr>

</tbody>

</table>

</div>

Qwen3-VL lineage — vision works via the bundled mmproj-F32.gguf projector with --mmproj (same LLM GGUF, no separate vision model). It's the Qwen3-VL projector (projection_dim 5120, matches this model's hidden size), shipped in this repo:

  --mmproj mmproj-F32.gguf \
  --image-min-tokens 1024     # REQUIRED — Qwen-VL needs >=1024 image tokens or it misreads fine detail

Without --image-min-tokens 1024 the server feeds too few image tokens and the model describes images incorrectly (right gist, wrong detail — the server even logs a warning at load). Verified on this model: a code label misread at default tokens read correctly once the flag was set.

<b>NOTE //</b> thinking model → for one-shot image Q&A use <code><|think_off|></code> (the bundled <code>chat_template.jinja</code>) or allow enough tokens, else the answer can come back empty. With <code>--mmproj</code> loaded the server disables the <code>--cache-reuse</code> feature (it logs <i>"cache_reuse is not supported by multimodal"</i>); whether ordinary cross-turn caching still helps with vision isn't something we've benchmarked.

</div>

This is the best speed/quality balance in ROCmFP4 — by design, not the absolute fastest. The two quality levers that are actually felt — genuine f16 token embeddings and a Q6_K output head — sit on the fast single-scale q4_0_rocmfp4_fast body. The alternatives we'd otherwise reach for (an all-dual-scale body, selective higher-precision tensors) buy a KL improvement that sits inside the measurement noise while costing decode speed — so the fast body is the right point. A leaner Q5-embedding build is a few tok/s faster but degrades the one lever you notice; we keep full f16 embeddings.

We ran this exact sweep in full on the sibling Qwen3.6-27B base card — every rocmfp4 lever measured by KL divergence vs the BF16 reference plus llama-bench decode. The frontier there is the same shape it is here: the fast body + f16 emb + Q6 head is the balance point, all-dual-scale and selective higher-precision land inside the noise, and the dynamic K-quant is the fidelity ceiling that rocmfp4's FP4 can't out-allocate. See that card for the numbers and the full experiments table; OBLITERATED follows the same recipe.

<b>WANT MAXIMUM FIDELITY INSTEAD OF SPEED?</b> There's no Unsloth UD-quant of this abliterated model, so for the last bit of fidelity grab a <b>Q6_K / Q8 GGUF of the base abliterated model</b> from <a href="https://huggingface.co/OBLITERATUS/Qwen3.6-27B-OBLITERATED"><b>OBLITERATUS/Qwen3.6-27B-OBLITERATED</b></a>. Those higher-bit GGUFs run on this same fork — trading lower KL for slower decode. We optimize for throughput in ROCmFP4; if you want fidelity over speed, that's the one to grab.

</div>

Grafted MTP head — measured. OBLITERATED ships no MTP head, so we transplanted a nextn block from a Qwen3.6-27B-MTP BF16 donor (output-lossless — the draft head only affects speed, never the tokens). Because OBLITERATED is abliterated from that same base, the borrowed head tracks it closely. Measured on the ROCmFP4 fork (llama-server --spec-type draft-mtp, f16 KV on target and drafter, 4 prompt types, temp 0.6):

<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Content</th>

<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Decode t/s (MTP)</th>

<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Draft acceptance</th>

</tr></thead>

<tbody>

<tr><td style="border:1px solid currentColor; padding:7px 10px;">math / reasoning</td><td style="border:1px solid currentColor; padding:7px 10px;">35.1</td><td style="border:1px solid currentColor; padding:7px 10px;">14.0</td><td style="border:1px solid currentColor; padding:7px 10px;">88.1%</td></tr>

<tr><td style="border:1px solid currentColor; padding:7px 10px;">technical</td><td style="border:1px solid currentColor; padding:7px 10px;">29.5</td><td style="border:1px solid currentColor; padding:7px 10px;">14.1</td><td style="border:1px solid currentColor; padding:7px 10px;">69.1%</td></tr>

<tr><td style="border:1px solid currentColor; padding:7px 10px;">prose / creative</td><td style="border:1px solid currentColor; padding:7px 10px;">24.6</td><td style="border:1px solid currentColor; padding:7px 10px;">14.0</td><td style="border:1px solid currentColor; padding:7px 10px;">52.2%</td></tr>

<tr><td style="border:1px solid currentColor; padding:7px 10px; font-weight:700;">average</td><td style="border:1px solid currentColor; padding:7px 10px; font-weight:700;">29.5</td><td style="border:1px solid currentColor; padding:7px 10px; font-weight:700;">14.1</td><td style="border:1px solid currentColor; padding:7px 10px; font-weight:700;">67.7%</td></tr>

</tbody>

</table>

</div>

~2.1× faster decode (14.1 → 29.5 t/s) at 67.7% token acceptance — high on structured/predictable text, lower on freeform prose, the profile of a borrowed (not natively-trained) head. KV is f16 on both the main model and the draft; the draft head is kept at 4-bit ROCmFP4.

The imatrix WINS here — measured. Quantized with an importance matrix from a public general+code calibration mix (Kalomaze groups_merged + froggeric code/technical, via froggeric/imatrix). Measured by KL-divergence + perplexity vs the true BF16 on a held-out general slice, imatrix vs no-imatrix:

<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Metric (vs BF16, held-out general)</th>

<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">No-imatrix</th>

<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Imatrix</th>

<th style="border:1px solid currentColor; padding:7px 10px; text-align:left; text-transform:uppercase; font-size:10px; letter-spacing:1px;">Change</th>

</tr></thead>

<tbody>

<tr><td style="border:1px solid currentColor; padding:7px 10px; font-weight:700;">Perplexity</td><td style="border:1px solid currentColor; padding:7px 10px;">+3.08%</td><td style="border:1px solid currentColor; padding:7px 10px; font-weight:700;">+2.58%</td><td style="border:1px solid currentColor; padding:7px 10px;">recovers ~16% of the 4-bit loss</td></tr>

<tr><td style="border:1px solid currentColor; padding:7px 10px; font-weight:700;">Median KLD</td><td style="border:1px solid currentColor; padding:7px 10px;">0.02070</td><td style="border:1px solid currentColor; padding:7px 10px; font-weight:700;">0.01843</td><td style="border:1px solid currentColor; padding:7px 10px; font-weight:700;">−11%</td></tr>

<tr><td style="border:1px solid currentColor; padding:7px 10px; font-weight:700;">Same top token as BF16</td><td style="border:1px solid currentColor; padding:7px 10px;">90.59%</td><td style="border:1px solid currentColor; padding:7px 10px; font-weight:700;">91.06%</td><td style="border:1px solid currentColor; padding:7px 10px;">+0.47 pp</td></tr>

</tbody>

</table>

</div>

For OBLITERATED the imatrix is a clean win on every robust metric — it behaves like its base Qwen3.6-27B, not like the dense Qwopus-Coder (where the same recipe worsened code-PPL — but that was a code metric; this is a general model measured on general text). Always measure; we did.

<b>NOTE //</b> Quality scope: the KL/PPL above is a fidelity-vs-BF16 measurement on ~20 K tokens of held-out general text, <b>not</b> an absolute benchmark. The MTP head is <i>borrowed</i> (not trained on this model) — output-lossless, affecting speed only. Experimental research build for AMD Strix Halo — hardware/driver/prompt-sensitive. <b>Not</b> native FP4 tensor-core execution.

</div>

# 0) convert the abliterated safetensors -> BF16 GGUF
python convert_hf_to_gguf.py OBLITERATED-27b/ --outtype bf16 --outfile OBLITERATED-BF16.gguf

# 1) ** GOTCHA ** OBLITERATED's config declares `mtp_num_hidden_layers: 1` but ships NO MTP weights,
#    so the convert labels it block_count=65 with only 64 real layers -> "missing tensor blk.64.*" on load.
#    The real model is 64 layers; the donor's nextn becomes the new blk.64.

# 2) graft a Qwen3.6-27B nextn head onto blk.64, then set block_count = 65
python inject_mtp_40b.py --target OBLITERATED-BF16.gguf --donor Qwen3.6-27B-MTP-BF16.gguf \
  --output OBLITERATED-MTP-BF16.gguf --source-layer 64 --dest-layer 64
python gguf_set_metadata.py OBLITERATED-MTP-BF16.gguf qwen35.block_count 65 --force

# 3) imatrix on the grafted BF16, then quant -> ROCmFP4 with genuine f16 embeddings
llama-imatrix -m OBLITERATED-MTP-BF16.gguf -f general+code-calib.txt -o obliterated.imatrix -c 512 -ngl 999
llama-quantize --token-embedding-type f16 --imatrix obliterated.imatrix \
  OBLITERATED-MTP-BF16.gguf  Qwen3.6-27B-OBLITERATED-MTP-ROCmFP4-STRIX-embF16-imatrix.gguf  Q4_0_ROCMFP4_STRIX

# headQ6 variant adds the Q6_K output head
llama-quantize --token-embedding-type f16 --output-tensor-type q6_K --imatrix obliterated.imatrix \
  OBLITERATED-MTP-BF16.gguf  Qwen3.6-27B-OBLITERATED-MTP-ROCmFP4-STRIX-embF16-imatrix-headQ6.gguf  Q4_0_ROCMFP4_STRIX

> Experimental research build for AMD Strix Halo — hardware/driver/prompt-sensitive, may not reproduce elsewhere. Not native FP4 tensor-core execution.

this ROCmFP4 quant  ──quantized──▶  OBLITERATUS/Qwen3.6-27B-OBLITERATED  ──abliterated──▶  Qwen/Qwen3.6-27B

A 4-bit Strix-Halo quant of OBLITERATUS's abliterated 27B. The abliteration (refusal-direction removal + source interpolation) is upstream work; we add the ROCmFP4 quant, genuine f16 embeddings, and a grafted MTP head.

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<tr><td style="border:1px solid currentColor; padding:8px 11px; width:26%;">BASE MODEL</td><td style="border:1px solid currentColor; padding:8px 11px;"><a href="https://huggingface.co/OBLITERATUS/Qwen3.6-27B-OBLITERATED">OBLITERATUS/Qwen3.6-27B-OBLITERATED</a> (Apache-2.0) · abliterated from <a href="https://huggingface.co/Qwen/Qwen3.6-27B">Qwen/Qwen3.6-27B</a> (Qwen team)</td></tr>

<tr><td style="border:1px solid currentColor; padding:8px 11px;">MTP DONOR</td><td style="border:1px solid currentColor; padding:8px 11px;">a Qwen3.6-27B-MTP <code>nextn</code> head (graft is output-lossless)</td></tr>

<tr><td style="border:1px solid currentColor; padding:8px 11px;">CALIBRATION</td><td style="border:1px solid currentColor; padding:8px 11px;">Kalomaze <code>groups_merged</code> + froggeric <code>code</code>/<code>technical</code> via <a href="https://huggingface.co/datasets/froggeric/imatrix">froggeric/imatrix</a></td></tr>

<tr><td style="border:1px solid currentColor; padding:8px 11px;">CHAT TEMPLATE</td><td style="border:1px solid currentColor; padding:8px 11px;"><a href="https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates">froggeric/Qwen-Fixed-Chat-Templates</a></td></tr>

<tr><td style="border:1px solid currentColor; padding:8px 11px;">FORMAT + RUNTIME</td><td style="border:1px solid currentColor; padding:8px 11px;"><a href="https://github.com/charlie12345/ROCmFPX">charlie12345/ROCmFPX</a> (llama.cpp, MIT)</td></tr>

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Sibling ROCmFP4 Strix Halo models — Qwen3.6-27B-MTP · Qwen3.6-35B-A3B-MTP · Qwopus3.6-27B-Coder-MTP · Qwen3.6-40B-Deckard-MTP · Qwen3-Coder-Next · Nex-N2-mini

Derivative quantization — verify the base model's license before redistribution / use.

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