vmlinux/Qwen3.5-122B-A10B-Heretic-ROCmFP4-iMatrix-GGUF

Model

19

stars

16

commits

3

linked in READMEs

Jul 26, 2026

updated

amd
conversational
endpoints_compatible
experimental
gguf
heretic
imatrix
mtp
qwen3.5
qwen3.5-moe
rocm
rocmfp4
speculative-decoding
text-generation
vulkan

README

Qwen3.5 122B-A10B Heretic · ROCmFP4 iMatrix

A compact, importance-calibrated ROCmFP4 build for high-memory AMD systems

**122B total · 10B active · 60.70 GiB · 28.45 tok/s native · 33.68 tok/s MTP · BF16 KLD 0.100716 · 28.11 Decode t/s ** Decode Speed 34.16% Faster Size 13.47gb Smaller.

[!IMPORTANT] This GGUF uses custom ROCmFP4 tensor types. It requires ROCmFPX or a runtime with equivalent support; stock llama.cpp, Ollama, LM Studio, and similar stock runtimes cannot load it.

I also suggest usign the template from https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates Froggeric is tha dude.

Downloads

ArtifactDirect download
Main iMatrix model — 60.70 GiBDownload Qwen3.5-122B-A10B-Heretic-ROCmFP4-iMatrix.gguf
Optional MTP companion — 2.14 GiBDownload Qwen3.5-122B-A10B-Heretic-ROCmFP4-MTP.gguf
Calibration matrix — 342.28 MiBDownload Qwen3.5-122B-A10B-Heretic-code-technical.imatrix

Browse every repository file →

What it is

This is an iMatrix-calibrated quantization of the full-weight Qwen3.5 122B-A10B Heretic checkpoint. It was built with ROCmFPX's compact Q4_0_ROCMFP4_STRIX_LEAN recipe, but the hardware-oriented preset name has been left out of the public filename.

Result
BF16 mean KLD0.100716 ± 0.003407
Two-sigma KLD upper bound0.107530
Improvement from iMatrix14.01% lower KLD
Greedy decode28.448 tok/s
Greedy decode with MTP33.677 tok/s
4,277-token prefill350.248 tok/s

Files

FilePurposeSizeSHA-256
Qwen3.5-122B-A10B-Heretic-ROCmFP4-iMatrix.ggufMain text model65,184,265,248 bytesf563fba693c65a05431d4beecd11736edf7e54208902cae70d7b725057a7556e
Qwen3.5-122B-A10B-Heretic-ROCmFP4-MTP.ggufOptional external MTP companion2,294,290,880 bytes5554538448efb668099be898ce7c8da5920e86b321f45e8ac1f7dca3518cc0a8
Qwen3.5-122B-A10B-Heretic-code-technical.imatrixCalibration matrix358,906,432 bytes3a2a0e0bea5fe380a27cc938ad02e727dcbd840744ab3ddb3a8e8402a67deb05
chat_template.jinjaPinned Qwen3.5 chat template7,756 bytesa4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715

The main model is complete and runs independently. The 23-tensor, one-layer MTP file is an optional external companion; no MTP layer is embedded in the main GGUF. Runtimes call this role a draft model, but Draft is omitted from the public filename because it does not mean unfinished.

Quality

The calibrated model and its uncalibrated control were replayed against the same saved distributions from the exact BF16 parent on ROCm0.

BuildMean KLDPPL ratioSame topRMS Δp
iMatrix0.100716 ± 0.0034071.080217 ± 0.01019887.629% ± 0.622%10.881%
Uncalibrated0.117119 ± 0.0041831.109251 ± 0.01129986.774% ± 0.640%12.077%

iMatrix calibration reduced mean KLD by 14.01%, reduced the PPL ratio by 0.029034, and improved same-top agreement by 0.855 percentage points. The two-sigma KLD upper bound is 0.107530, clearing the selected 0.126 performance-oriented target.

The paired replay covers 11 complete 512-token chunks and 2,805 evaluated next-token distributions. These were recovered at an exact probability-block boundary from an interrupted 32-chunk BF16 run. This is useful preliminary evidence, not a completed 32-chunk acceptance gate.

Performance

Measured on a 128 GB AMD Strix Halo system with Vulkan/RADV, 131,072 context, parallel 1, batch/ubatch 2048/1024, Q8_0 KV cache, flash attention, and MTP off. Values are medians from repeated runs.

WorkloadRepeatsDecodePrompt processing
Greedy, 256 generated tokens528.448 tok/s71.929 tok/s
Sampled, 256 generated tokens328.418 tok/s71.294 tok/s
4,277-token prefill + 128 generated328.041 tok/s350.248 tok/s

Relative to the otherwise matched uncalibrated build, decode improved by 2.43%, 5.23%, and 2.84% respectively. Long-prompt prefill changed by -1.03%. These are single-system measurements, not general performance guarantees.

Optional MTP acceleration

The external MTP companion was tested against this exact iMatrix trunk in a matched MTP-off/on A/B. The model, runtime, context, cache, batch geometry, prompts, and companion identity were held constant; only MTP execution changed.

WorkloadNative decodeMTP decodeDecode gainAcceptanceEnd-to-end gain
Greedy, 256 tokens28.328 tok/s33.677 tok/s+18.89%85.03%+16.44%
Sampled, 256 tokens28.035 tok/s30.953 tok/s+10.41%80.00%+8.76%
4,277-token prefill + 12827.585 tok/s33.752 tok/s+22.36%90.73%-0.45%

MTP reduced prompt-processing throughput by about 7% because the companion must catch up on the prompt. It therefore helps most when generation is long enough to repay that fixed cost. The exact identity probe was unchanged, and both the MTP-off and MTP-on lanes passed technical prose, executable code semantics, strict JSON, tool-calling, balanced-analysis, and defensive-safety smokes.

Quantization

The model was quantized once from the validated BF16 GGUF using a 339-chunk code/technical importance matrix and the Q4_0_ROCMFP4_STRIX_LEAN preset.

Tensor typeCount
Q4_0_ROCMFP4_FAST457
Q4_0_ROCMFP460
F32361
Q5_K1

In this preset, attention K/V tensors retain dual-scale ROCmFP4 protection, most transformer weights use the compact FAST layout, and token embeddings/output use Q5_K. The artifact contains 879 tensors across 48 blocks and no embedded MTP layer.

Run

llama-server \
  --model Qwen3.5-122B-A10B-Heretic-ROCmFP4-iMatrix.gguf \
  --host 127.0.0.1 --port 8080 \
  -dev Vulkan0 --n-gpu-layers 999 \
  --ctx-size 131072 --parallel 1 \
  --flash-attn on --batch-size 2048 --ubatch-size 1024 \
  --cache-type-k q8_0 --cache-type-v q8_0 \
  --jinja --reasoning-format deepseek

Adjust context and cache settings for your memory budget. The tested runtime was charlie12345/ROCmFPX commit a6a93765f7ce9779c13f9881164a65f7a9f31198, built in Release mode for gfx1151 with Vulkan and HIP enabled, plus a local Qwen3.5 MoE graph correction. Results with newer ROCmFPX revisions have not yet been established.

To enable the optional companion, append:

  --spec-type draft-mtp \
  --spec-draft-model Qwen3.5-122B-A10B-Heretic-ROCmFP4-MTP.gguf \
  --spec-draft-device Vulkan0 --spec-draft-ngl 999 \
  --spec-draft-type-k f16 --spec-draft-type-v f16 \
  --spec-draft-n-max 2 --spec-draft-p-min 0.6 \
  --spec-draft-p-split 0.10 --spec-draft-backend-sampling

Tested system

PlatformAMD Strix Halo, 128 GB unified memory
GPU targetRadeon 8060S / gfx1151
BackendVulkan / RADV
KernelLinux 6.17.0-1028-oem
Mesa25.2.8

No vision projector is included; treat this release as text-only.

Lineage and credits

This repository exists because several people published substantial work openly. Their roles are kept distinct so downstream users can find the original sources.

Please preserve this lineage, the Apache 2.0 license, and a description of your changes when redistributing derivatives.

License

The distributed model derivative is provided under the upstream Apache License 2.0. The included LICENSE is copied from the pinned full-weight source. Runtime and tooling repositories retain their own licenses: Heretic software is AGPL-3.0, while ROCmFPX and the deployment tooling are separately licensed. No runtime source code is bundled in this model repository.

Contributors

vmlinux

16 commits

vmlinux/Qwen3.5-122B-A10B-Heretic-ROCmFP4-iMatrix-GGUF

Model

19

stars

16

commits

3

linked in READMEs

Jul 26, 2026

updated

amd
conversational
endpoints_compatible
experimental
gguf
heretic
imatrix
mtp
qwen3.5
qwen3.5-moe
rocm
rocmfp4
speculative-decoding
text-generation
vulkan

README

Qwen3.5 122B-A10B Heretic · ROCmFP4 iMatrix

A compact, importance-calibrated ROCmFP4 build for high-memory AMD systems

**122B total · 10B active · 60.70 GiB · 28.45 tok/s native · 33.68 tok/s MTP · BF16 KLD 0.100716 · 28.11 Decode t/s ** Decode Speed 34.16% Faster Size 13.47gb Smaller.

[!IMPORTANT] This GGUF uses custom ROCmFP4 tensor types. It requires ROCmFPX or a runtime with equivalent support; stock llama.cpp, Ollama, LM Studio, and similar stock runtimes cannot load it.

I also suggest usign the template from https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates Froggeric is tha dude.

Downloads

ArtifactDirect download
Main iMatrix model — 60.70 GiBDownload Qwen3.5-122B-A10B-Heretic-ROCmFP4-iMatrix.gguf
Optional MTP companion — 2.14 GiBDownload Qwen3.5-122B-A10B-Heretic-ROCmFP4-MTP.gguf
Calibration matrix — 342.28 MiBDownload Qwen3.5-122B-A10B-Heretic-code-technical.imatrix

Browse every repository file →

What it is

This is an iMatrix-calibrated quantization of the full-weight Qwen3.5 122B-A10B Heretic checkpoint. It was built with ROCmFPX's compact Q4_0_ROCMFP4_STRIX_LEAN recipe, but the hardware-oriented preset name has been left out of the public filename.

Result
BF16 mean KLD0.100716 ± 0.003407
Two-sigma KLD upper bound0.107530
Improvement from iMatrix14.01% lower KLD
Greedy decode28.448 tok/s
Greedy decode with MTP33.677 tok/s
4,277-token prefill350.248 tok/s

Files

FilePurposeSizeSHA-256
Qwen3.5-122B-A10B-Heretic-ROCmFP4-iMatrix.ggufMain text model65,184,265,248 bytesf563fba693c65a05431d4beecd11736edf7e54208902cae70d7b725057a7556e
Qwen3.5-122B-A10B-Heretic-ROCmFP4-MTP.ggufOptional external MTP companion2,294,290,880 bytes5554538448efb668099be898ce7c8da5920e86b321f45e8ac1f7dca3518cc0a8
Qwen3.5-122B-A10B-Heretic-code-technical.imatrixCalibration matrix358,906,432 bytes3a2a0e0bea5fe380a27cc938ad02e727dcbd840744ab3ddb3a8e8402a67deb05
chat_template.jinjaPinned Qwen3.5 chat template7,756 bytesa4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715

The main model is complete and runs independently. The 23-tensor, one-layer MTP file is an optional external companion; no MTP layer is embedded in the main GGUF. Runtimes call this role a draft model, but Draft is omitted from the public filename because it does not mean unfinished.

Quality

The calibrated model and its uncalibrated control were replayed against the same saved distributions from the exact BF16 parent on ROCm0.

BuildMean KLDPPL ratioSame topRMS Δp
iMatrix0.100716 ± 0.0034071.080217 ± 0.01019887.629% ± 0.622%10.881%
Uncalibrated0.117119 ± 0.0041831.109251 ± 0.01129986.774% ± 0.640%12.077%

iMatrix calibration reduced mean KLD by 14.01%, reduced the PPL ratio by 0.029034, and improved same-top agreement by 0.855 percentage points. The two-sigma KLD upper bound is 0.107530, clearing the selected 0.126 performance-oriented target.

The paired replay covers 11 complete 512-token chunks and 2,805 evaluated next-token distributions. These were recovered at an exact probability-block boundary from an interrupted 32-chunk BF16 run. This is useful preliminary evidence, not a completed 32-chunk acceptance gate.

Performance

Measured on a 128 GB AMD Strix Halo system with Vulkan/RADV, 131,072 context, parallel 1, batch/ubatch 2048/1024, Q8_0 KV cache, flash attention, and MTP off. Values are medians from repeated runs.

WorkloadRepeatsDecodePrompt processing
Greedy, 256 generated tokens528.448 tok/s71.929 tok/s
Sampled, 256 generated tokens328.418 tok/s71.294 tok/s
4,277-token prefill + 128 generated328.041 tok/s350.248 tok/s

Relative to the otherwise matched uncalibrated build, decode improved by 2.43%, 5.23%, and 2.84% respectively. Long-prompt prefill changed by -1.03%. These are single-system measurements, not general performance guarantees.

Optional MTP acceleration

The external MTP companion was tested against this exact iMatrix trunk in a matched MTP-off/on A/B. The model, runtime, context, cache, batch geometry, prompts, and companion identity were held constant; only MTP execution changed.

WorkloadNative decodeMTP decodeDecode gainAcceptanceEnd-to-end gain
Greedy, 256 tokens28.328 tok/s33.677 tok/s+18.89%85.03%+16.44%
Sampled, 256 tokens28.035 tok/s30.953 tok/s+10.41%80.00%+8.76%
4,277-token prefill + 12827.585 tok/s33.752 tok/s+22.36%90.73%-0.45%

MTP reduced prompt-processing throughput by about 7% because the companion must catch up on the prompt. It therefore helps most when generation is long enough to repay that fixed cost. The exact identity probe was unchanged, and both the MTP-off and MTP-on lanes passed technical prose, executable code semantics, strict JSON, tool-calling, balanced-analysis, and defensive-safety smokes.

Quantization

The model was quantized once from the validated BF16 GGUF using a 339-chunk code/technical importance matrix and the Q4_0_ROCMFP4_STRIX_LEAN preset.

Tensor typeCount
Q4_0_ROCMFP4_FAST457
Q4_0_ROCMFP460
F32361
Q5_K1

In this preset, attention K/V tensors retain dual-scale ROCmFP4 protection, most transformer weights use the compact FAST layout, and token embeddings/output use Q5_K. The artifact contains 879 tensors across 48 blocks and no embedded MTP layer.

Run

llama-server \
  --model Qwen3.5-122B-A10B-Heretic-ROCmFP4-iMatrix.gguf \
  --host 127.0.0.1 --port 8080 \
  -dev Vulkan0 --n-gpu-layers 999 \
  --ctx-size 131072 --parallel 1 \
  --flash-attn on --batch-size 2048 --ubatch-size 1024 \
  --cache-type-k q8_0 --cache-type-v q8_0 \
  --jinja --reasoning-format deepseek

Adjust context and cache settings for your memory budget. The tested runtime was charlie12345/ROCmFPX commit a6a93765f7ce9779c13f9881164a65f7a9f31198, built in Release mode for gfx1151 with Vulkan and HIP enabled, plus a local Qwen3.5 MoE graph correction. Results with newer ROCmFPX revisions have not yet been established.

To enable the optional companion, append:

  --spec-type draft-mtp \
  --spec-draft-model Qwen3.5-122B-A10B-Heretic-ROCmFP4-MTP.gguf \
  --spec-draft-device Vulkan0 --spec-draft-ngl 999 \
  --spec-draft-type-k f16 --spec-draft-type-v f16 \
  --spec-draft-n-max 2 --spec-draft-p-min 0.6 \
  --spec-draft-p-split 0.10 --spec-draft-backend-sampling

Tested system

PlatformAMD Strix Halo, 128 GB unified memory
GPU targetRadeon 8060S / gfx1151
BackendVulkan / RADV
KernelLinux 6.17.0-1028-oem
Mesa25.2.8

No vision projector is included; treat this release as text-only.

Lineage and credits

This repository exists because several people published substantial work openly. Their roles are kept distinct so downstream users can find the original sources.

Please preserve this lineage, the Apache 2.0 license, and a description of your changes when redistributing derivatives.

License

The distributed model derivative is provided under the upstream Apache License 2.0. The included LICENSE is copied from the pinned full-weight source. Runtime and tooling repositories retain their own licenses: Heretic software is AGPL-3.0, while ROCmFPX and the deployment tooling are separately licensed. No runtime source code is bundled in this model repository.

Contributors

vmlinux

16 commits