Instructions to use singulared/Qwen3.8-27B-ROCmFP4-MTP-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 singulared/Qwen3.8-27B-ROCmFP4-MTP-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 singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
# Run inference directly in the terminal:
llama cli -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
# Run inference directly in the terminal:
llama cli -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-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 singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
# Run inference directly in the terminal:
./llama-cli -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-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 singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF

Use Docker

docker model run hf.co/singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF

LM Studio
Jan

vLLM

How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "singulared/Qwen3.8-27B-ROCmFP4-MTP-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": "singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF

Ollama
How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF with Ollama:
```
ollama run hf.co/singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
```

Unsloth Studio

How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-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 singulared/Qwen3.8-27B-ROCmFP4-MTP-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 singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF to start chatting

How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-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": "singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF

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 "singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF" \
  --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 singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF
```

Lemonade

How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF

Run and chat with the model

lemonade run user.Qwen3.8-27B-ROCmFP4-MTP-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use singulared/Qwen3.8-27B-ROCmFP4-MTP-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 singulared/Qwen3.8-27B-ROCmFP4-MTP-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 singulared/Qwen3.8-27B-ROCmFP4-MTP-GGUF

Run Hermes

hermes

Atomic Chat

Qwen3.8-27B-ROCmFP4-MTP-GGUF / README.md

singulared

Model card: ROCmFP4 builds + MTP drafter ladder with acceptance data

0a95e53 verified about 9 hours ago

preview code

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metadata

license: apache-2.0
base_model:
  - Qwen/Qwen3.8-27B
  - ggml-org/Qwen3.8-27B-GGUF
base_model_relation: quantized
library_name: llama.cpp
pipeline_tag: text-generation
tags:
  - gguf
  - rocm
  - rocmfpx
  - rocmfp4
  - amd
  - strix-halo
  - gfx1151
  - mtp
  - speculative-decoding
  - qwen3.5

Qwen3.8-27B — ROCmFP4 + MTP drafter ladder (Strix Halo)

ROCmFP4 builds of Qwen3.8-27B, quantised from ggml-org/Qwen3.8-27B-GGUF's BF16 (sha256 5a3eedc837bcbd13…, verified), plus an MTP drafter at five precisions so the speculative-decoding numbers below can be reproduced rather than taken on trust.

Other ROCmFP4 builds of this model already exist. What is here and (at publication) not elsewhere: draft-acceptance rates, a per-backend n-max sweep, a -ub sweep, and a ROCm 7.2 vs 10.1 comparison that reverses the preset ranking.

⚠ Read this before choosing FP4. On the same machine, mainline llama.cpp on Vulkan with a plain Q4_K_M and the official MTP drafter is faster — 330 vs 227 prefill (+43%), decode tied. These FP4 files are worth it for the 1.7 GiB smaller footprint (15.6 vs 17.3 GiB resident), which matters when co-residing two models. They are not the throughput winner.

Files

file	preset	size
`Qwen3.8-27B-ROCMFP4-STRIX.gguf`	`Q4_0_ROCMFP4_STRIX`	13.75 GiB
`Qwen3.8-27B-ROCMFP4-COHERENT.gguf`	`Q4_0_ROCMFP4_COHERENT`	14.41 GiB
`mtp-Qwen3.8-27B-ROCMFP4-STRIX.gguf`	FP4 drafter	1.85 GiB
`mtp-Qwen3.8-27B-ROCMFP3.gguf`	FP3 drafter	1.55 GiB
`mtp-Qwen3.8-27B-ROCMFP6.gguf`	FP6 drafter	2.27 GiB
`mtp-Qwen3.8-27B-ROCMFP8.gguf`	FP8 drafter	2.86 GiB
`mtp-Qwen3.8-27B-ROCMFP2.gguf`	FP2 drafter — broken, see below	1.48 GiB

Requires a ROCmFPX build; mainline llama.cpp does not know the Q4_0_ROCMFP4_* tensor types.

Hardware / method

AMD Ryzen AI MAX+ 395, Radeon 8060S (gfx1151, RDNA 3.5), 124 GiB GTT, Debian sid, kernel 7.2. Server-measured (llama-server + probe), ~8K-token prompt, temperature 0, one job at a time. ROCm nightly pinned to therock-dist-linux-gfx1151-10.1.0a20260815.

1. Draft depth (`--spec-draft-n-max`) is per-backend

n-max	Vulkan Q4_K_M decode	acc	FP4 decode	acc
3	—	—	33.42	100.0%
4	35.80	88.1%	35.16	98.7%
5	38.94	91.6%	38.67	98.1%
6	38.47	86.5%	38.04	97.5%
7	37.84	82.0%	39.26	94.7%
8	28.56	78.1%	32.36	95.2%
10	25.47	59.4%	—	—

Acceptance decays monotonically with depth; the knee is where verifying rejected drafts costs more than the accepted ones save. FP4 holds higher acceptance, so its knee sits deeper and flatter. This is not DeepSeek-V4's n=2 — draft depth does not transfer between models.

2. Drafter precision is a bandwidth lever, not a quality one

Target fixed, drafter varied, Vulkan, n=5:

drafter	size	decode	acceptance
Q4_K_M	1.89 GiB	39.16	91.6%
Q6_K	2.28 GiB	38.28	92.1%
Q5_K_M	2.08 GiB	36.93	89.0%
Q8_0	2.95 GiB	34.74	89.0%

Acceptance is flat (89–92%) while decode spans 13% — so shrinking the drafter buys bandwidth and costs nothing in draft quality. Advice to keep drafters at ≥Q8 does not hold here.

3. FP2 destroys a drafter

FPX ladder, STRIX target, ROCm 10.1, n=5:

drafter	decode	acceptance
FP4-STRIX	37.03	97.4%
FP3	36.07	98.1%
FP6	30.92	96.6%
FP8	29.76	96.6%
FP2	22.07	64.0%

FP2's codebook has no exact zero. This drafter is BF16-sourced — the case usually assumed safe — and acceptance still collapses. Do not use FP2 for a draft model.

4. The preset ranking flips with the ROCm version

llama-bench, pp2048:

preset	ROCm 7.2.4	ROCm 10.1 nightly
COHERENT	205.7	208.6 (+1%)
STRIX	151.8	272.0 (+79%)

COHERENT wins on 7.2; STRIX wins on 10.1. Any "preset X is best" claim — including ones in other repos — is conditional on a ROCm version that usually goes unstated.

5. `-ub 256`, not the default

`-ub`	Vulkan pp2048	FP4 pp2048 (10.1)
256	370.6	300.3
512	360.9	267.3
1024	343.7	236.5
2048	332.0	235.8

Both backends prefer a small micro-batch; -ub 2048 costs FP4 27% of its prefill. Batch size (-b 512→4096) changes nothing.

Usage

llama-server \
  -m Qwen3.8-27B-ROCMFP4-STRIX.gguf \
  -md mtp-Qwen3.8-27B-ROCMFP4-STRIX.gguf \
  --spec-type draft-mtp --spec-draft-n-max 5 \
  -ngl 99 -ngld 99 -fa on -ub 256

Provenance

Every file derives from ggml-org/Qwen3.8-27B-GGUF, sha256-verified before quantisation: Qwen3.8-27B-BF16.gguf = 5a3eedc837bcbd13…, mtp-Qwen3.8-27B-BF16.gguf = 5723e551c4ee2b8c….

Qwen3.8-27B — ROCmFP4 + MTP drafter ladder (Strix Halo)

Files

Hardware / method

1. Draft depth (--spec-draft-n-max) is per-backend

2. Drafter precision is a bandwidth lever, not a quality one

3. FP2 destroys a drafter

4. The preset ranking flips with the ROCm version

5. -ub 256, not the default

Usage

Provenance

1. Draft depth (`--spec-draft-n-max`) is per-backend

5. `-ub 256`, not the default