Instructions to use vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16

Use Docker

docker model run hf.co/vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16

LM Studio
Jan

vLLM

How to use vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16

Ollama
How to use vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF with Ollama:
```
ollama run hf.co/vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16
```

Unsloth Studio

How to use vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF to start chatting

How to use vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF with Docker Model Runner:
```
docker model run hf.co/vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16
```

Lemonade

How to use vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16

Run and chat with the model

lemonade run user.Muse-Glimmer-30B-heretic-ROCmFPX-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF:BF16

Run Hermes

hermes

Atomic Chat

Muse-Glimmer-30B-heretic-ROCmFPX-GGUF / README.md

vmlinux

Publish Muse Glimmer 30B Heretic ROCmFPX GGUF release

99f8189 verified about 22 hours ago

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metadata

base_model:
  - darkc0de/Muse-Glimmer-30B-heretic
  - meta-models/Muse-Glimmer-30B-assistant
base_model_relation: quantized
library_name: llama.cpp
pipeline_tag: image-text-to-text
license: apache-2.0
tags:
  - gguf
  - llama.cpp
  - rocm
  - rocmfpx
  - amd
  - strix-halo
  - gfx1151
  - imatrix
  - multimodal
  - muse-glimmer
  - conversational
  - heretic
  - uncensored
  - decensored
  - abliterated
  - dflash
  - speculative-decoding

Muse-Glimmer-30B Heretic ROCmFPX GGUF

ROCmFP4 and ROCmFP8 builds of darkc0de/Muse-Glimmer-30B-heretic, targeted and tested on AMD Strix Halo (gfx1151). The source is a reproducible Heretic v1.4.0 abliteration of Meta's Muse Glimmer 30B. Rawr. 🦖

These are custom ROCmFPX formats, not ordinary llama.cpp Q4/Q8 files. Read the compatibility section before downloading.

Experimental runtime required: these GGUFs need a patched build of charlie12345/ROCmFPX. Stock llama.cpp does not implement the ROCmFP4/ROCmFP8 tensor layouts, while the pinned ROCmFPX base predates Muse Glimmer support. Apply the included ROCmFPX-Muse-Glimmer.patch to ROCmFPX commit 00d54526e…, then build that checkout.

Files

Click a filename to download it directly from the Hub.

File	ROCmFPX preset	Size	BPW	iMatrix
`Muse-Glimmer-30B-heretic-ROCmFP4.gguf`	`Q4_0_ROCMFP4_STRIX`	14.17 GiB	4.36	Yes
`Muse-Glimmer-30B-heretic-ROCmFP4-Q6-QUALITY.gguf`	`Q4_0_ROCMFP4_COHERENT`	14.93 GiB	4.60	Yes
`Muse-Glimmer-30B-heretic-ROCmFP8.gguf`	`Q8_0_ROCMFPX`	26.77 GiB	8.25	No
`mmproj-Muse-Glimmer-30B-heretic-BF16.gguf`	BF16 vision projector	3.58 GiB	—	—
`Muse-Glimmer-30B-DFlash-ROCmFP4.gguf`	`Q4_0_ROCMFP4_STRIX` drafter	1.39 GiB	4.63	No
`Muse-Glimmer-30B-DFlash-ROCmFP8.gguf`	`Q8_0_ROCMFPX` drafter	2.47 GiB	8.25	No

Suggested choices:

ROCmFP4: default Strix Halo speed/quality build. Fast FP4 body, dual-scale FP4 attention K/V, and Q6_K token embeddings.
ROCmFP4-Q6-QUALITY: coherence-biased build. Dual-scale FP4 throughout the body with Q6_K token embeddings.
ROCmFP8: high-fidelity 8.25-bpw reference build.
DFlash ROCmFP8: recommended drafter for this Heretic target on the tested host. It produced the best measured six-token speculative speed.
DFlash ROCmFP4: smaller drafter alternative.

The fresh Heretic-labelled BF16 projector works with all three text models.

About the abliteration

The source model was produced with Heretic v1.4.0 and retains Muse Glimmer's architecture, tokenizer, chat template, perception encoder, and native 131,072-token context. The source author reports KL divergence 0.0743 from the original and 11 refusals out of 100, compared with 59/100 for the original.

Published abliteration parameters:

Parameter	Value
`direction_index`	38.49
`attn.o_proj.max_weight`	1.46
`attn.o_proj.max_weight_position`	30.98
`attn.o_proj.min_weight`	0.75
`attn.o_proj.min_weight_distance`	29.88
`mlp.down_proj.max_weight`	1.49
`mlp.down_proj.max_weight_position`	31.58
`mlp.down_proj.min_weight`	0.32
`mlp.down_proj.min_weight_distance`	26.40

See the source model card for its reproduction recipe and the original author's description. These figures describe the BF16 source; this repository did not rerun that evaluation on each quantization.

iMatrix

Both FP4 targets use the same GGUF importance matrix:

500 chunks × 512 tokens (approximately 256k calibration tokens)
416 tensor importance entries consumed by each quantizer
varied narrative/general-language calibration corpus
checkpoints saved every 100 chunks

Q8_0_ROCMFPX does not consume importance weights, so the FP8 reference was intentionally built without an iMatrix.

The two DFlash files are quantizations of Meta's official Muse-Glimmer-30B-assistant, not a separately trained Heretic assistant. Every proposal is still verified by the Heretic target. Compatibility and speed were measured rather than assumed; see Validation below.

Compatibility

These files use experimental ROCmFPX tensor types and will not load in stock llama.cpp.

The validated runtime was built from:

ROCmFPX base commit 00d54526e24e3aba4c76474e3147cbf9c7cc034c
upstream llama.cpp Muse converter commit d2f83055d6e3b379b5d34c4837122a918cf402c2
the included Muse text, vision, DFlash, sparse-attention, and quantized-cache compatibility patch

The runtime was built with ROCm and Vulkan backends. Reported generation tests used ROCm0 on gfx1151.

Minimal runtime setup:

git clone https://github.com/charlie12345/ROCmFPX.git
cd ROCmFPX
git checkout 00d54526e24e3aba4c76474e3147cbf9c7cc034c

hf download vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF \
  ROCmFPX-Muse-Glimmer.patch \
  --local-dir /tmp/muse-glimmer-heretic-rocmfpx
git apply /tmp/muse-glimmer-heretic-rocmfpx/ROCmFPX-Muse-Glimmer.patch

BUILD_DIR=build-muse-rocmfpx \
JOBS=16 \
CMAKE_HIP_COMPILER=/opt/rocm-7.2.0/lib/llvm/bin/clang++ \
GGML_HIP_ROCWMMA_FATTN=OFF \
./scripts/build-strix-rocmfp4-mtp.sh

Adjust CMAKE_HIP_COMPILER for the installed ROCm version. The patch must be applied to the exact pinned commit.

Download and run

hf download vmlinux/Muse-Glimmer-30B-heretic-ROCmFPX-GGUF \
  Muse-Glimmer-30B-heretic-ROCmFP4.gguf \
  Muse-Glimmer-30B-DFlash-ROCmFP8.gguf \
  mmproj-Muse-Glimmer-30B-heretic-BF16.gguf \
  --local-dir ./Muse-Glimmer-30B-heretic-ROCmFPX

Text:

./llama-cli \
  -m ./Muse-Glimmer-30B-heretic-ROCmFPX/Muse-Glimmer-30B-heretic-ROCmFP4.gguf \
  -dev ROCm0 -ngl all -c 8192 -cnv

Vision:

./llama-cli \
  -m ./Muse-Glimmer-30B-heretic-ROCmFPX/Muse-Glimmer-30B-heretic-ROCmFP4.gguf \
  -mm ./Muse-Glimmer-30B-heretic-ROCmFPX/mmproj-Muse-Glimmer-30B-heretic-BF16.gguf \
  --image ./image.png \
  -p "Describe this image." \
  -dev ROCm0 -ngl all -c 8192 -cnv -st

DFlash speculative decoding (recommended starting point):

./llama-cli \
  -m ./Muse-Glimmer-30B-heretic-ROCmFPX/Muse-Glimmer-30B-heretic-ROCmFP4.gguf \
  --spec-draft-model ./Muse-Glimmer-30B-heretic-ROCmFPX/Muse-Glimmer-30B-DFlash-ROCmFP8.gguf \
  --spec-type draft-dflash \
  -dev ROCm0 -ngl all \
  --spec-draft-device ROCm0 --spec-draft-ngl all \
  -ctk q4_0 -ctv q4_0 \
  --spec-draft-type-k q4_0 --spec-draft-type-v q4_0 \
  --spec-draft-n-max 6 --spec-draft-n-min 0 \
  --spec-draft-p-min 0.0 --spec-draft-p-split 0.10 \
  --no-spec-draft-backend-sampling \
  -c 8192 -cnv

Validation

All three target quantizations loaded on ROCm0 with every layer offloaded and generated tokens. The default FP4 also passed an end-to-end image test using the fresh projector; it correctly identified both the Hugging Face site and the repository shown in the test screenshot.

Three deterministic 256-token runs compared native ROCmFP4 decoding with the official FP8 DFlash. Both used Q4_0 target KV cache, seed 42, temperature 0, a 4,096-token context, six-token drafts, and the same prompts.

Prompt	Native tok/s	DFlash tok/s	Accepted / proposed
Technical explanation	13.8	22.4	155 / 595
Backup strategy	13.7	24.1	163 / 543
Fiction opening	13.8	21.0	149 / 628
Mean / aggregate	13.77	22.50	467 / 1,766 (26.44%)

The measured mean speedup was 1.63×. At least one draft token was accepted in 199 of 297 verification rounds (67.0%), and the mean accepted span including the target token was 2.57 tokens. In a technical-prompt comparison, the smaller FP4 DFlash reached 20.3 tok/s with 140/684 proposals accepted, so FP8 is the recommended drafter on this host.

Native and speculative greedy outputs were not byte-identical. With custom ROCmFP4 kernels, speculative verification changes target batch shapes; small floating-point differences can redirect the model's reasoning trace. Treat this cross-checkpoint pairing as a measured throughput option, not a claim of bit-exact decoding.

Additional verification:

all 13 downloaded Heretic Safetensors shards passed the publisher's manifest
both FP4 targets consumed all 416 iMatrix entries
all six release files pass the published SHA256SUMS
source, intermediate, calibration, and patch hashes are retained in PROVENANCE_SHA256SUMS
detailed build and validation report: BUILD_RESULTS.md

DFlash, not MTP

The companion files are converted from Meta's official five-layer MuseGlimmerAssistantModel, which uses DFlash block diffusion with a trained block size of 16. Run it with --spec-type draft-dflash; it is not an MTP checkpoint and should not be run with draft-mtp.

Provenance

Heretic source revision: 64a36ddcb9745b521bd9eb114465c93f860a594f
Original target revision: f84ecc3a0ea984a4c04542a84269e3d065350a6e
DFlash source revision: 2c86316d689027b91123638739743fef1d425233
ROCmFPX base: 00d54526e24e3aba4c76474e3147cbf9c7cc034c
Conversion: upstream llama.cpp d2f83055d6e3b379b5d34c4837122a918cf402c2

The source model's Apache 2.0 license and Muse Glimmer usage policy apply. This abliterated model may be more likely to produce unsafe, objectionable, or unreliable output; deployers should perform their own evaluation and add guardrails appropriate to their application.