⚠️ 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).

Derivative of Qwen3.6-35B-A3B, 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 Qwen3.6-35B-A3B. 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 produced with different size-quality tradeoffs
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

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

git clone https://github.com/ciru-ai/ROCmFPX.git ROCmFPX
cd ROCmFPX
git checkout 68f23f34c12d7e61177a034b0d8d3fea2129565e
# then build per the fork's own README

GGUF Files

File	Size	Quant
Qwen3.6-35B-A3B-ROCMFPX-MQ-Q4.gguf	22.7 GB	MagicQuant Q4 layout in ROCmFPX types (hybrid, fork-only)

Usage

Requires a from-source build of the ROCmFPX fork (stock llama.cpp, LM Studio, and Ollama cannot load these files):

bash

# Interactive chat (--jinja uses the model's embedded chat template)
llama-cli -m Qwen3.6-35B-A3B-ROCMFPX-MQ-Q4.gguf -c 8192 --jinja -cnv

# Server mode
llama-server -m Qwen3.6-35B-A3B-ROCMFPX-MQ-Q4.gguf -c 8192 --port 8080 -ngl 99 -fa on --jinja

Serving: MTP Speculative Decoding

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):

llama-server -m Qwen3.6-35B-A3B-ROCMFPX-MQ-Q4.gguf -c 8192 --port 8080 --host 127.0.0.1 -ngl 99 -md Qwen3.6-35B-A3B-ROCMFPX-MQ-Q4.gguf --spec-type draft-mtp -ctk q8_0 -ctv q8_0 -fa on

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

Generated with MagicQuant

lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF

Qwen3.6-35B-A3B-ROCmFPX-GGUF

⚠️ These files do NOT load on standard llama.cpp