Instructions to use kingjones777/Qwen3.5-27B-ROCmFP4-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 kingjones777/Qwen3.5-27B-ROCmFP4-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 kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf kingjones777/Qwen3.5-27B-ROCmFP4-GGUF: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 kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf kingjones777/Qwen3.5-27B-ROCmFP4-GGUF: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 kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use kingjones777/Qwen3.5-27B-ROCmFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kingjones777/Qwen3.5-27B-ROCmFP4-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": "kingjones777/Qwen3.5-27B-ROCmFP4-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/kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP

Ollama
How to use kingjones777/Qwen3.5-27B-ROCmFP4-GGUF with Ollama:
```
ollama run hf.co/kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP
```

Unsloth Studio

How to use kingjones777/Qwen3.5-27B-ROCmFP4-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 kingjones777/Qwen3.5-27B-ROCmFP4-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 kingjones777/Qwen3.5-27B-ROCmFP4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kingjones777/Qwen3.5-27B-ROCmFP4-GGUF to start chatting

How to use kingjones777/Qwen3.5-27B-ROCmFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP

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": "kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kingjones777/Qwen3.5-27B-ROCmFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use kingjones777/Qwen3.5-27B-ROCmFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Qwen3.5-27B-ROCmFP4-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use kingjones777/Qwen3.5-27B-ROCmFP4-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 kingjones777/Qwen3.5-27B-ROCmFP4-GGUF: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 kingjones777/Qwen3.5-27B-ROCmFP4-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kingjones777/Qwen3.5-27B-ROCmFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/Qwen3.5-27B-ROCmFP4-GGUF: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 "kingjones777/Qwen3.5-27B-ROCmFP4-GGUF: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"

Qwen3.5-27B — ROCmFP4 / ROCmFPX GGUF

First ROCmFP4/ROCmFPX quantization of Qwen/Qwen3.5-27B. Four quants, built from the Hub BF16 GGUF (no re-conversion), each individually verified on real hardware.

Built for AMD Strix Halo (gfx1151) — Ryzen AI MAX+ 395, 128 GB unified — with the ROCmFPX llama.cpp fork.

⚠️ Needs a ROCmFPX-capable llama.cpp build. These will not load in stock llama.cpp / Ollama / LM Studio.

Variants

file	ftype	size	`token_embd`	decode	correctness
`Qwen3.5-27B-Q4_0_ROCMFP4_COHERENT.gguf`	102	14.72 GiB	Q6_K	12.08 t/s	3/3
`Qwen3.5-27B-Q6_0_ROCMFPX_AGENT.gguf`	114	23.22 GiB	Q8_0	7.11 t/s	3/3
`Qwen3.5-27B-Q8_0_ROCMFPX.gguf`	111	25.92 GiB	Q8_0	7.17 t/s	3/3
`Qwen3.5-27B-Q8_0_ROCMFPX_AGENT.gguf`	115	26.28 GiB	Q8_0	7.22 t/s	3/3

mmproj-BF16.gguf is included — required for image input (-fa off).

Which to pick: the 4-bit (102) — fastest and smallest, same answers as the 8-bits.

⚠️ Q6_0_ROCMFPX_AGENT (114) is the weakest choice here: larger than the 4-bit and slower than the plain 8-bit. The AGENT recipe raises speculative-draft acceptance, and Qwen3.5-27B ships no drafter, so that benefit cannot apply. It is included for completeness.

Measured — not estimated

Hardware: Ryzen AI MAX+ 395 (Strix Halo, gfx1151), 128 GB unified. -ngl 999 -c 4096 -fa on -fit off. 300 tokens, warm-up discarded, median of 3.

ftype	run 1 / 2 / 3	median	spread
102	12.09 12.04 12.08	12.08	1.004
114	7.06 7.11 7.11	7.11	1.007
111	7.11 7.17 7.17	7.17	1.008
115	7.13 7.22 7.24	7.22	1.015

No MTP/EAGLE drafter ships with this model, so these are the deployed numbers with nothing left on the table.

Speculative decoding

Qwen ships no official MTP/draft head for Qwen3.5-27B — verified against the base repo file list and config.json (unlike Qwen3.8-27B, which does). The decode figures above are therefore no-speculation numbers.

⭐ However, classic draft-model speculation should work, because every Qwen3.5 model shares the same vocabulary (248320) and architecture (qwen35):

llama-server -m Qwen3.5-27B-Q4_0_ROCMFP4_COHERENT.gguf \
  --spec-type draft-simple --model-draft Qwen3.5-0.8B-Q4_0_ROCMFP4_COHERENT.gguf \
  --spec-draft-ngl 99 --spec-draft-n-max 3 -ngl 999 -c 4096 -fa on -fit off

⚠️ We have not measured this — the box was needed elsewhere before the test completed. It is offered as a documented, plausible route, not a benchmarked claim. If you try it, the levers that mattered on comparable models were --spec-draft-n-max (sweep 2–5) and using the smallest drafter that still accepts well.

Third-party heads exist (VirVen/Qwen3.5-27B-EAGLE3-v2, selimaktas/Flow-Drafter-Qwen3.5-27B) but are SGLang-specific or research artifacts and do not load in llama.cpp.

Head protection

Qwen3.5-27B does not tie embeddings — it has a real output.weight, and both it and token_embd are protected (Q6_K on the 4-bit, Q8_0 on the rest). Audited by exact tensor name. Note the smaller Qwen3.5 models do tie, so the same flags behave differently across this family — pass both.

  15804651168  Qwen3.5-27B-Q4_0_ROCMFP4_COHERENT.gguf
  24936068768  Qwen3.5-27B-Q6_0_ROCMFPX_AGENT.gguf
  27834807968  Qwen3.5-27B-Q8_0_ROCMFPX.gguf
  28214425248  Qwen3.5-27B-Q8_0_ROCMFPX_AGENT.gguf

Credits

Base model: Qwen/Qwen3.5-27B
Quantization stack: ROCmFPX

Downloads last month: 219

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

View +1 variant

Model tree for kingjones777/Qwen3.5-27B-ROCmFP4-GGUF

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