Instructions to use gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-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 gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-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 gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-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 gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-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 gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./llama-cli -hf gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-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 gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Use Docker

docker model run hf.co/gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

LM Studio
Jan

vLLM

How to use gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-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": "gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-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/gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Ollama
How to use gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF with Ollama:
```
ollama run hf.co/gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
```
Unsloth Desktop

How to use gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF: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": "gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF with Docker Model Runner:
```
docker model run hf.co/gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP
```

Lemonade

How to use gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-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 gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-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 gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-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 "gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-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"

Qwen3.6-35B-A3B — ROCmFP4 STRIX_LEAN quant for AMD Strix Halo

Q4_0_ROCMFP4_STRIX_LEAN GGUF quantization of Qwen/Qwen3.6-35B-A3B (Qwen3.5-MoE architecture, ~3B active, multimodal), produced for AMD Strix Halo (Ryzen AI Max, gfx1151).

⚠️ These files use ROCmFP4 tensor types (ggml type 106) — stock llama.cpp will reject them (invalid ggml type). Use either:

kyuz0/amd-strix-halo-toolboxes rocm-7.2.4-rocmfp4 image (public, maintained), or

the Hal0ai/Hal0_ROCmFPX llama.cpp fork.

First published STRIX_LEAN of this model as far as I can tell (a ROCMFP4_FAST and an MTP-focused ROCmFP4 exist elsewhere — the STRIX_LEAN recipe trades a hair of file size for the Strix attn-K/V quality layout + Q5_K token embeddings).

Files

File	Quant	BPW	Size	Notes
`Qwen3.6-35B-A3B-Q4_0_ROCMFP4_STRIX_LEAN.gguf`	Q4_0_ROCMFP4_STRIX_LEAN	4.29	19.0 GB	Fits a 64 GB carve-out with long context to spare
`mmproj-BF16.gguf`	BF16	—	0.90 GB	Vision projector — load alongside the quant
`imatrix_unsloth.gguf_file`	—	—	184 MB	Importance matrix used for calibration (from unsloth)

Measured performance

AMD Ryzen AI Max+ 395 (Strix Halo, 128 GB unified LPDDR5X), ROCm backend, kyuz0 rocm-7.2.4-rocmfp4 toolbox, llama-bench -fa 1 --mmap 0:

test	tok/s
pp512	1102
tg128	63.1

For reference, the same model as UD-Q4_K_XL runs 48 tok/s on identical hardware — the ROCmFP4 kernels are worth ~+31% decode on Strix Halo.

How it was made

BF16 GGUF source and imatrix from unsloth/Qwen3.6-35B-A3B-MTP-GGUF:

llama-quantize --imatrix imatrix_unsloth.gguf_file \
  Qwen3.6-35B-A3B-BF16-00001-of-00002.gguf \
  Qwen3.6-35B-A3B-Q4_0_ROCMFP4_STRIX_LEAN.gguf \
  Q4_0_ROCMFP4_STRIX_LEAN

(nextn/MTP metadata from the source survives conversion, though speculative decoding showed no speedup on Strix Halo in my testing — plain decode is recommended.)

Serving

llama-server -m Qwen3.6-35B-A3B-Q4_0_ROCMFP4_STRIX_LEAN.gguf \
  --mmproj mmproj-BF16.gguf -ngl 999 -fa on --jinja -c 65536

Companion repos

Credits

Base model: Qwen — Qwen3.6-35B-A3B (Apache-2.0)
BF16 GGUF conversion + imatrix: unsloth
ROCmFP4 quant formats: Hal0ai/Hal0_ROCmFPX; maintained builds: kyuz0

Downloads last month: 500

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Model tree for gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(774)

this model