Instructions to use ArtomYuan/Qwen3.8-27B-ROCmFPX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ArtomYuan/Qwen3.8-27B-ROCmFPX 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 ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP

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 ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP

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 ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP

Use Docker

docker model run hf.co/ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP

LM Studio
Jan
Ollama
How to use ArtomYuan/Qwen3.8-27B-ROCmFPX with Ollama:
```
ollama run hf.co/ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP
```
Unsloth Desktop

How to use ArtomYuan/Qwen3.8-27B-ROCmFPX with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ArtomYuan/Qwen3.8-27B-ROCmFPX with Docker Model Runner:
```
docker model run hf.co/ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP
```

Lemonade

How to use ArtomYuan/Qwen3.8-27B-ROCmFPX with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Qwen3.8-27B-ROCmFPX-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use ArtomYuan/Qwen3.8-27B-ROCmFPX with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP

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 ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ArtomYuan/Qwen3.8-27B-ROCmFPX with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP

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 "ArtomYuan/Qwen3.8-27B-ROCmFPX:Q4_0_ROCMFP" \
  --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"

Qwen3.8-27B-ROCmFPX

ROCmFPX quantized GGUF (7 tiers + vision mmproj) of Qwen/Qwen3.8-27B — official BF16 weights, locally requantized.

⚡ Quick Start

Requires strix.cpp (this fork supports ROCmFPX; upstream llama.cpp cannot load these files — format note at the end).

# Text only (smallest tier: Q4_FAST ~13.6 GiB)
strix-server -m Qwen3.8-27B_Q4_0_ROCMFP4_FAST.gguf -ngl 99 -fa on --port 8080

# Vision: add --mmproj mmproj-Qwen3.8-27B_BF16.gguf
# All tiers fit on a single 128 GB machine (largest: 26.7 GiB)

🎯 Which tier? (3-second decision)

Fastest: Q4_0_ROCMFP4_FAST (13.6 GiB)
Speed + stabler quality: Q4_0_ROCMFP4_STRIX (14.0 GiB — recommended sweet spot)
Higher-bit reference: Q6_0_ROCMFPX (21.0 GiB)
Agent / tool-call: Q6_0_ROCMFPX_AGENT (23.6 GiB) / Q8_0_ROCMFPX_AGENT (26.7 GiB)
Quality first: Q8_0_ROCMFPX (26.3 GiB)

Model Overview

Base model: Qwen3.8-27B (dense, official weights by Alibaba)
Quantization: BF16 -> ROCmFP4 / ROCmFPx, 7 variants
MTP: GGUFs carry an MTP head — enable with strix-server --spec-type draft-mtp (speculative decoding)
Vision: multimodal — pair with the bundled mmproj (see Usage)

Files

File	Size
Qwen3.8-27B_Q4_0_ROCMFP4_FAST.gguf	13.56 GiB (14,562,235,840 B)
Qwen3.8-27B_Q4_0_ROCMFP4_FAST_COHERENT.gguf	13.90 GiB (14,929,749,440 B)
Qwen3.8-27B_Q4_0_ROCMFP4_STRIX.gguf	13.98 GiB (15,013,963,200 B)
Qwen3.8-27B_Q6_0_ROCMFPX.gguf	20.98 GiB (22,528,382,400 B)
Qwen3.8-27B_Q6_0_ROCMFPX_AGENT.gguf	23.55 GiB (25,283,188,160 B)
Qwen3.8-27B_Q8_0_ROCMFPX.gguf	26.26 GiB (28,193,396,160 B)
Qwen3.8-27B_Q8_0_ROCMFPX_AGENT.gguf	26.69 GiB (28,664,108,480 B)
mmproj-Qwen3.8-27B_BF16.gguf	0.87 GiB (931,145,952 B)

SHA-256 checksums for all files: see sha256.txt.

Quantization Tiers — what's here & how to choose

All files are builds of the same official weights through llama-quantize (strix.cpp fork); upstream llama.cpp cannot load them (see Format Note). Quick guide:

Q4_0_ROCMFP4_FAST — smallest & fastest baseline (4.25 bpw). Bulk weights in single-scale FP4.
Q4_0_ROCMFP4_FAST_COHERENT — FAST + Q6_K token embeddings (better coherence, +0.2 bpw).
Q4_0_ROCMFP4_STRIX — FAST + Q6_K embeddings + dual-scale attention K/V (quality-biased; same speed class as FAST — the recommended quality/speed sweet spot).
Q6_0_ROCMFPX / Q8_0_ROCMFPX — higher-bit reference layouts (6.50 / 8.25 bpw). Decode is slower than the Q4 family on this dense model (Q6's unpack overhead is the heaviest).
Q6_0_ROCMFPX_AGENT / Q8_0_ROCMFPX_AGENT — agent / tool-call oriented routing variants (attention & FFN boosts on sensitive layers).

The full type ladder lives in the strix.cpp repo (llama-quantize --help).

Benchmarks (llama-bench)

Measured on AMD Strix Halo (gfx1151), llama-bench -ngl 99 -p 256,2048 -n 256 -r 5, strix.cpp build 11107. (Dense 27B — all parameters active, so tg is inherently lower than MoE models. Each figure is the mean of the last 4 of 5 reps — the first rep carries one-time warm-up cost and is excluded.)

Variant	tg256 (t/s)	pp256 (t/s)	pp2048 (t/s)	Size	bpw
Q4_0_ROCMFP4_FAST	14.05	418.0	398.9	13.6 GiB	4.25
Q4_0_ROCMFP4_FAST_COHERENT	13.99	404.0	389.8	13.9 GiB	~4.45
Q4_0_ROCMFP4_STRIX	14.02	400.2	385.4	14.0 GiB	~4.49
Q6_0_ROCMFPX	9.45	312.1	309.3	21.0 GiB	6.50
Q6_0_ROCMFPX_AGENT	8.70	335.6	330.4	23.6 GiB	6.50
Q8_0_ROCMFPX	7.93	371.8	365.6	26.3 GiB	8.25
Q8_0_ROCMFPX_AGENT	7.83	368.9	363.0	26.7 GiB	8.25

Usage

# Text only:
strix-server -m Qwen3.8-27B_Q4_0_ROCMFP4_FAST.gguf -ngl 99 -fa on --port 8080

# Vision (multimodal; --mmproj enables image input):
strix-server -m Qwen3.8-27B_Q4_0_ROCMFP4_FAST.gguf --mmproj mmproj-Qwen3.8-27B_BF16.gguf -ngl 99 -fa on --port 8080

Swap -m to any other variant file to run that variant (see Files).
OpenAI-compatible API served at http://127.0.0.1:8080/v1.
MTP head: load with strix-server --spec-type draft-mtp for speculative MTP acceleration.

Format Note (ROCmFPX)

ROCmFPX is a quantization format family (strix.cpp extension: GGML types 100–107 / FTYPE 100–119) — upstream llama.cpp cannot load these files. Load them with strix.cpp, a llama.cpp fork that supports all upstream GGUF formats plus ROCmFP4/ROCmFPx natively.

License & Disclaimer

Base model: Qwen3.8-27B (Alibaba) — Apache-2.0
This quantized version: Apache-2.0 (ArtomYuan)

This is a third-party community quantization (not an official Qwen release), not affiliated with Alibaba / Qwen. Provided for research and learning; use is subject to the base model license (Apache-2.0). No warranty is provided.

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GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

View +2 variants

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