Instructions to use alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST 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 alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST:Q4_0_ROCMFP

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST:Q4_0_ROCMFP
# Run inference directly in the terminal:
llama cli -hf alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST:Q4_0_ROCMFP

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST:Q4_0_ROCMFP
# Run inference directly in the terminal:
./build/bin/llama cli -hf alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST:Q4_0_ROCMFP

Use Docker

docker model run hf.co/alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST:Q4_0_ROCMFP

Llama new
LM Studio
Jan
Ollama
How to use alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST with Ollama:
```
ollama run hf.co/alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST:Q4_0_ROCMFP
```
Unsloth Desktop

How to use alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST: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": "alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST:Q4_0_ROCMFP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST with Docker Model Runner:
```
docker model run hf.co/alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST:Q4_0_ROCMFP
```

Lemonade

How to use alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST:Q4_0_ROCMFP

Run and chat with the model

lemonade run user.Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST-Q4_0_ROCMFP

List all available models

lemonade list

Hermes Agent

How to use alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST: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 alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST:Q4_0_ROCMFP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST: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 "alst10/Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST: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"

Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST

File size: 1,416 Bytes

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---
license: cc-by-4.0
base_model: ManniX-ITA/Qwen3.6-27B-A3B-Coder
tags:
- gguf
- rocm
- rocmfpx
- amd
- llama.cpp
---

# Qwen3.6-27B-A3B-Coder-ROCMFP4_FAST - ROCmFPX Quantized

This model was dynamically quantized and generated using the [ROCmFPX My Repo Space](https://huggingface.co/spaces/alst10/ROCmFPX-my-repo)
* **Original Base Model:** [ManniX-ITA/Qwen3.6-27B-A3B-Coder](https://huggingface.co/ManniX-ITA/Qwen3.6-27B-A3B-Coder)

## Usage Instructions with ROCmFPX llama.cpp

To run this model, you must use the specialized [ROCmFPX fork of llama.cpp](https://github.com/charlie12345/ROCmFPX) by charlie12345. Standard `llama.cpp` releases do not currently support ROCmFPX quantization formats.

```bash
# 1. Clone the ROCmFPX repository
git clone --depth 1 [https://github.com/charlie12345/ROCmFPX.git](https://github.com/charlie12345/ROCmFPX.git)
cd ROCmFPX

# 2. Build the project
cmake -B build -G Ninja -DGGML_CUDA=OFF -DGGML_NATIVE=OFF
cmake --build build --target llama-cli -j

# 3. Run the model
./build/bin/llama-cli -m Qwen3.6-27B-A3B-Coder-Q4_0_ROCMFP4_FAST.gguf -p "You are a helpful assistant. Hello!" -n 128
```

## License
This model is distributed under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license. Please adhere to the original license constraints of the base model and provide appropriate attribution when sharing.