Instructions to use christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF",
	filename="AEON-Ultimate-ROCmFP4-STRIX-MTP.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-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 christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF
# Run inference directly in the terminal:
llama cli -hf christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF
# Run inference directly in the terminal:
llama cli -hf christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF

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 christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF
# Run inference directly in the terminal:
./llama-cli -hf christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF

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 christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF

Use Docker

docker model run hf.co/christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF

LM Studio
Jan
Ollama
How to use christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF with Ollama:
```
ollama run hf.co/christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF
```

Unsloth Studio

How to use christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-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 christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-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 christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF to start chatting

How to use christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF

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": "christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-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 christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF

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 christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF with Docker Model Runner:
```
docker model run hf.co/christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF
```

Lemonade

How to use christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF

Run and chat with the model

lemonade run user.AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

AEON Ultimate ROCmFP4/ROCmFP6 GGUF for AMD Strix Halo

This repo contains AMD Strix Halo GGUF quantizations of AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16, including the bundled Qwen3.6 MTP tensors for llama.cpp speculative decoding.

These files require custom Strix Halo runtimes. They are not for stock llama.cpp and not for vLLM.

ROCmFP4 runtime: charlie12345/rocmfp4-llama, branch mtp-rocmfp4-strix.
ROCmFP6 runtime: charlie12345/ROCmFPX, tested at commit 11d76c2.

Files

File	Format	Use	Size	SHA256
`AEON-Ultimate-ROCmFP4-STRIX_LEAN-MTP.gguf`	ROCmFP4 STRIX_LEAN	Recommended FP4 speed/default build	14.8 GiB	`9afd704c1fc85789c86f9b8099aa736952af13967b10a3ad11f6c2306aeaa9e2`
`AEON-Ultimate-ROCmFP4-STRIX-MTP.gguf`	ROCmFP4 STRIX	Quality-biased FP4 preset	15.0 GiB	`9dc52099a62ce4cdb113e6383eed0efa5b726ed928c21bc9caa107f2ce9a8cb2`
`AEON-Ultimate-ROCmFP6-MTP.gguf-00001-of-00006.gguf` through `00006-of-00006`	`Q6_0_ROCMFPX` split GGUF	Recommended FP6 speed/default build; load the first shard	6 shards, 20.97 GiB total	See `SHA256SUMS`
`AEON-Ultimate-ROCmFP6-AGENT-MTP.gguf-00001-of-00007.gguf` through `00007-of-00007`	`Q6_0_ROCMFPX_AGENT` split GGUF	Quality-biased FP6 profile for coding harnesses and tool-call style workloads; load the first shard	7 shards, 23.54 GiB total	See `SHA256SUMS`

All variants were quantized from the BF16 source GGUF with the MTP extension bundled. Do not requantize from the FP4 files.

Which File Should I Use?

For lowest latency on one Strix Halo, start with AEON-Ultimate-ROCmFP4-STRIX_LEAN-MTP.gguf. It had the best measured MTP decode speed in my coding-agent tests.

For a higher-quality FP6 experiment, start with AEON-Ultimate-ROCmFP6-AGENT-MTP.gguf-00001-of-00007.gguf. It preserves more tensors at Q8 under ROCmFPX's agent routing and is the most plausible coding-harness candidate among the FP6 files. It is slower than default FP6 in a short decode smoke test.

The FP6 variants are uploaded as split GGUFs with shards under 4 GB to avoid large multipart-upload issues. Put all shards for a variant in the same directory and load the 00001-of-... file; llama.cpp will find the remaining shards.

For parallel subagent pools, test no speculative decoding as well as MTP. In my FP4 tests, MTP helped single-stream latency but did not scale well across four parallel slots.

Build the ROCmFP4 Runtime

git clone https://github.com/charlie12345/rocmfp4-llama.git
cd rocmfp4-llama
git checkout mtp-rocmfp4-strix
env JOBS=16 scripts/build-strix-rocmfp4-mtp.sh

Build the ROCmFP6 Runtime

git clone https://github.com/charlie12345/ROCmFPX.git
cd ROCmFPX
git checkout 11d76c2
env JOBS=16 scripts/build-strix-rocmfp4-mtp.sh llama-cli llama-server llama-bench

The build directory is still named build-strix-rocmfp4 because ROCmFPX extends the same Strix Halo llama.cpp fork.

Common Strix Halo environment:

export HSA_OVERRIDE_GFX_VERSION=11.5.1
export GGML_HIP_ENABLE_UNIFIED_MEMORY=1
export LD_LIBRARY_PATH=$PWD/build-strix-rocmfp4/bin:/opt/rocm/lib:${LD_LIBRARY_PATH:-}

Recommended FP6 Coding Server

ROCmFPX includes a helper that auto-checks MTP metadata and starts llama-server with conservative draft-mtp settings:

cd ROCmFPX
MODEL=/path/to/AEON-Ultimate-ROCmFP6-AGENT-MTP.gguf-00001-of-00007.gguf \
DEVICE=ROCm0 \
SPEC_DRAFT_DEVICE=ROCm0 \
CTX_SIZE=32768 \
PARALLEL=1 \
SPEC_DRAFT_N_MAX=4 \
PERF_PRESET=latency \
scripts/run-rocmfpx-mtp-server.sh

The helper runs with --reasoning off, --reasoning-format none, Jinja templates, full offload, flash attention, F16 KV cache, and draft-mtp enabled. That matters for OpenAI-compatible coding harnesses: with reasoning enabled, Qwen-style reasoning can be routed to reasoning_content while normal content appears empty to clients.

Equivalent core server args:

./build-strix-rocmfp4/bin/llama-server \
  -m /path/to/AEON-Ultimate-ROCmFP6-AGENT-MTP.gguf-00001-of-00007.gguf \
  --alias aeon-fp6-agent --host 127.0.0.1 --port 8080 \
  --jinja --reasoning off --reasoning-format none \
  -c 32768 -ngl 999 -fa on -dev ROCm0 \
  -b 2048 -ub 512 -t 16 -tb 32 \
  -ctk f16 -ctv f16 \
  --spec-type draft-mtp \
  --spec-draft-device ROCm0 --spec-draft-ngl all \
  --spec-draft-type-k f16 --spec-draft-type-v f16 \
  --spec-draft-n-max 4 --spec-draft-n-min 0 \
  --spec-draft-p-min 0.0 --spec-draft-p-split 0.10 \
  --parallel 1 --metrics --no-webui

Recommended FP4 Single-Stream Coding Server

This was the fastest tested single-stream coding-agent config:

./build-strix-rocmfp4/bin/llama-server \
  -m /path/to/AEON-Ultimate-ROCmFP4-STRIX_LEAN-MTP.gguf \
  --alias aeon --host 127.0.0.1 --port 8080 \
  --jinja -rea off \
  -c 32768 -ngl 999 -fa on -dev ROCm0 \
  -b 512 -ub 512 -t 16 -tb 32 \
  -ctk f16 -ctv f16 \
  --spec-type draft-mtp \
  --spec-draft-device ROCm0 --spec-draft-ngl all \
  --spec-draft-type-k f16 --spec-draft-type-v f16 \
  --spec-draft-n-max 4 --spec-draft-n-min 0 --spec-draft-p-min 0.0 \
  --parallel 1 --metrics --no-mmap

Recommended Parallel Subagent Server

For a second Strix Halo dedicated to several lower-priority subagents, test no speculative decoding with four parallel slots:

./build-strix-rocmfp4/bin/llama-server \
  -m /path/to/AEON-Ultimate-ROCmFP4-STRIX_LEAN-MTP.gguf \
  --alias aeon --host 127.0.0.1 --port 8081 \
  --jinja -rea off \
  -c 32768 -ngl 999 -fa on -dev ROCm0 \
  -b 512 -ub 512 -t 16 -tb 32 \
  -ctk f16 -ctv f16 \
  --parallel 4 --metrics --no-mmap

Measured Strix Halo Performance

Hardware: Ryzen AI Max+ 395 / Radeon 8060S, gfx1151, 128 GB unified memory.

FP4 runtime: rocmfp4-llama branch mtp-rocmfp4-strix, commit 4795079b plus a small local converter fix. Single cold coding prompt, 256 generated tokens:

Config	Decode tok/s	Draft accepted
no speculative decoding	13.95	n/a
`draft-mtp`, n-max 2	30.24	164 / 182
`draft-mtp`, n-max 3	36.10	185 / 209
`draft-mtp`, n-max 4	38.46	195 / 238
`draft-mtp`, n-max 5	38.10	201 / 266
`draft-mtp`, n-max 6	36.80	205 / 295

Longer-context FP4 checks with the recommended single-stream config:

Prompt tokens	Prefill tok/s	Decode tok/s	Generated
6,357	about 299-302	22.61	128
12,584	294.10	23.32	128
25,000	255.14	16.31	96

Parallel/subagent FP4 serving, varied coding prompts, 256 generated tokens per request:

Mode	Parallel	Aggregate decode tok/s	Avg per-request decode tok/s
MTP n-max 4	2	23.87	15.55
MTP n-max 4	4	21.62	7.61
no spec	2	22.28	12.58
no spec	4	28.89	9.97

FP6 runtime smoke test: ROCmFPX commit 11d76c2, llama-bench -dev ROCm0 -ngl 999 -fa on -p 64 -n 64 -r 1:

Quant	Size	Prompt processing tok/s	Text generation tok/s
`Q6_0_ROCMFPX`	20.97 GiB	57.82	9.44
`Q6_0_ROCMFPX_AGENT`	23.54 GiB	57.66	8.65

These FP6 numbers are loader/throughput smoke tests, not full coding-agent MTP acceptance sweeps.

STRIX vs STRIX_LEAN

STRIX is a quality-biased ROCmFP4 preset and STRIX_LEAN is a speed/size-biased preset. They are quantizations of the same AEON Ultimate weights.

On the tested Strix Halo, STRIX did not improve speed or MTP acceptance:

Quant	Prompt	Decode tok/s	Draft accepted
STRIX_LEAN	short coding	38.46	195 / 238
STRIX	short coding	38.28	195 / 238
STRIX_LEAN	8k-class prompt	22.61	79 / 189
STRIX	8k-class prompt	20.30	73 / 209

Use STRIX_LEAN unless your own correctness eval shows a quality win for STRIX.

Provenance

Source model: AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16
Base model: Qwen/Qwen3.6-27B
FP4 runtime fork: charlie12345/rocmfp4-llama
FP6 runtime fork: charlie12345/ROCmFPX
BF16-to-GGUF conversion source revision: d52a7bc77bf9c6c1bcff638bf20037e8dcb9af5b
ROCmFPX quantization revision: 11d76c2

License

Apache-2.0, inherited from the source model and Qwen/Qwen3.6-27B.

Downloads last month: 3,835

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for christopher-kapic/AEON-Ultimate-ROCmFP4-Strix-Halo-GGUF

Base model

Qwen/Qwen3.6-27B

Finetuned

AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16

Quantized

(34)

this model