Instructions to use gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF",
	filename="Qwen3.6-35B-A3B-STRIX_LEAN-DFLASH.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

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

Use Docker

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

LM Studio
Jan

vLLM

How to use gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-DFLASH-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-DFLASH-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-DFLASH-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

How to use gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-DFLASH-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 gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-DFLASH-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 gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF to start chatting

How to use gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-DFLASH-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-DFLASH-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": "gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Run Hermes

hermes

Atomic Chat new

OpenClaw new

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-DFLASH-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Qwen3.6-35B-A3B — ROCmFP4 STRIX_LEAN, DFlash baked in

A single-file, self-accelerating GGUF: the model and its DFlash speculative-decoding draft are merged into one .gguf. No --model-draft, no --spec-type flag — point -m at this file and speculative decoding just happens.

To our knowledge, the first "draft-included" GGUF publication anywhere.

llama-server -m Qwen3.6-35B-A3B-STRIX_LEAN-DFLASH.gguf -ngl 999 -fa on --jinja -c 65536

Requirements

This needs both a ROCmFP4-aware build and the DFlash-graft support for embedded drafts — neither exists upstream yet. Use:

gsrunion/rocmfp4-llama branch dflash-graft (built and validated on AMD Strix Halo / gfx1151), or
your own build once the fixes below land in charlie12345/rocmfp4-llama

On first load the server extracts the draft's tensors to a small cached sidecar file next to the model (one-time, ~1 second).

Measured performance

AMD Ryzen AI Max+ 395 (Strix Halo, 128 GB unified LPDDR5X), server-timing, self-accelerating load (zero extra flags):

	tok/s	acceptance
Baked single-file	91.8	98.5% (405/411)
Two-file (`--model-draft` + flags)	96.0	97–98%
Plain LEAN, no draft	63.1	—

Within noise of the two-file config — the merge adds no overhead.

How it was made

The draft's tensors are merged into the target GGUF prefixed dflash.* (target keeps its own tensor names untouched — no collision, no size overhead: DFlash drafts already borrow the target's token embeddings and output head at runtime, so nothing is duplicated). A dflash.embedded marker key flags the file for auto-detection.

Two fixes were needed in the serving fork to make this work (both filed against the base fork, worth watching if you hit similar issues building your own):

The tensor-count sanity check in the model loader didn't allow "extra" tensors belonging to a sibling model in the same file — even though the check already had unused plumbing for exactly this case.
The draft's mask_token_id (namespaced under tokenizer.* by convention, though it's actually draft-specific) has to be copied into the merged file explicitly, or drafting silently no-ops with zero speedup and no error.

Base weights: gsrunion/Qwen3.6-35B-A3B-ROCmFP4-STRIX_LEAN-GGUF. Draft: z-lab/Qwen3.6-35B-A3B-DFlash.

Credits

Base model: Qwen — Qwen3.6-35B-A3B (Apache-2.0)
DFlash draft: z-lab
ROCmFP4 quant formats: Hal0ai; fork base: charlie12345

Downloads last month: 239

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

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