Engine requirement: these GGUFs use ROCmFPX tensor types added to the fork in
August 2026. A fork build newer than 2026-08-06 is required; older builds fail to
load with tensor 'output.weight' has invalid ggml type 102. Stock llama.cpp cannot
load these files at all -- for stock llama.cpp use the sibling
MagicQuant repo.
Measured quality (2026-08-16, wikitext-2 PPL, ctx 512, 100 chunks, BF16
baseline 6.7443): MQ-Q6 measures 6.7412 — a tie with the stock Q6_K
(6.7470). MQ-Q4 measures 6.9240 (+2.7% vs baseline), a real quality loss
against its MagicQuant source (Q4_K_M: 6.7522). If quality at Q4 size matters
more than fork-native types, use the sibling repo's Q4_K_M; MQ-Q4 remains the
right pick only where the fork's FP4 execution path is the point.
⚠️ These files do NOT load on standard llama.cpp
They use AMD-native *_ROCMFPX tensor types from the experimental
ciru-ai/ROCmFPX llama.cpp fork (build from source).
For files that work with stock llama.cpp / LM Studio / Ollama, use the sibling repo:
lmcoleman/Qwen3.8-27B-MagicQuant-GGUF.
Derivative of Qwen3.8-27B, quantized using MagicQuant hybrid evolutionary per-tensor search and quantized to AMD-native ROCmFPX formats (fork-only) tuned for Strix Halo (gfx1151).
Base Model
This is a derivative of Qwen3.8-27B.
All credit for the base model architecture and weights goes to the original authors.
The base model's license applies to this derivative.
Quantization Method
Quantized using MagicQuant hybrid evolutionary per-tensor quantization,
based on the methodology by magiccodingman:
Tensors are classified into sensitivity groups (Embeddings, Head, Query, Key, Output, FFN Up/Down, MoE Experts, Router)
An evolutionary search finds the optimal quantization type per group, balancing size vs. perplexity
Q4/Q5/Q6 tier targets are searched, and each one ships only if it earns its place (see below)
Small-row tensors and sensitivity-critical layers (embeddings, output head, router) are kept at F32/F16/BF16
This is NOT a uniform quantization -- each tensor group gets its own optimal type
A tier name here is a size band, not a promise that every tensor uses that
exact type. A "Q5" is whatever mix of schemes landed in the Q5 size band with
the lowest measured perplexity loss -- which is the point of the search.
Tiers this build does not produce
Q5 -- rendering MagicQuant's Q5 config into ROCmFPX types predicts 20.68 GiB against a 50.89 GiB BF16 baseline (ratio 0.4063), which is the Q6 band, not Q5.
These were not built at all. This is a property of how the schemes round into the ROCmFPX type ladder for this particular model, not a temporary gap, so a file for them will not appear in a later build either.
ROCmFPX (AMD-native, fork-only)
These GGUFs use AMD-native quantization schemes from the experimental
ciru-ai/ROCmFPX llama.cpp fork,
tuned for and benchmarked on AMD Strix Halo (Radeon 8060S iGPU, gfx1151, unified memory):
ROCmFP3/4/6/8 tensor types with straight and "agent" presets (agent presets keep
tool-calling / JSON-structured output reliable at low bit-widths)
Files load only on the fork -- it is an experimental upstream research
build, so build from the pinned commit that produced these files (the
default branch may have moved on since):
bash
1git clone https://github.com/ciru-ai/ROCmFPX.git ROCmFPX
2cd ROCmFPX
3git checkout 68f23f34c12d7e61177a034b0d8d3fea2129565e
4# then build per the fork's own README
Perplexity measured on wikitext-2 (100 chunks, ctx 512) against the BF16 baseline of 6.7443. Lower is better; the percentage is the increase over BF16. These are the same measurements the tier selection is based on, so a tier that shipped is one that earned its size.
Recommended: Q4 (14.64 GiB). It is the smallest tier that is statistically tied with the best measured quality here. Q6 is 41% larger for 0.048 percentage points of perplexity, which is below what this measurement can resolve -- so the extra bytes buy nothing you can detect.
Usage
Requires a from-source build of the ROCmFPX fork
(stock llama.cpp, LM Studio, and Ollama cannot load these files):
bash
1# Interactive chat (--jinja uses the model's embedded chat template)2llama-cli -m Qwen3.8-27B-ROCMFPX-MQ-Q4.gguf -c 8192 --jinja -cnv
34# Server mode5llama-server -m Qwen3.8-27B-ROCMFPX-MQ-Q4.gguf -c 8192 --port 8080 -ngl 99 -fa on --jinja
This model includes MTP ("nextn") draft tensors, enabling self-speculative
decoding -- measured ~1.6-1.9x faster generation with a ~95% first-token
accept rate (no separate draft model needed; it drafts from itself):
Memory cost: MTP needs its own draft context alongside the main context,
so serving with it uses roughly 2x the model's memory compared to serving
without -md/--spec-type draft-mtp.
Caveats
The base model's license (apache-2.0) applies to all derivative files
Fork-only files: stock llama.cpp, LM Studio, and Ollama cannot load these -- build ciru-ai/ROCmFPX from source
Quantization reduces precision -- verify outputs for your specific use case
The hybrid quantization assigns different precision to different tensor groups, which means quality characteristics may differ from uniform quantizations
Limitations
Quantized models may exhibit subtle differences from the full-precision fine-tune
This model inherits any limitations and biases present in the base model
