Instructions to use 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-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 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-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 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX
# Run inference directly in the terminal:
llama cli -hf 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX
# Run inference directly in the terminal:
llama cli -hf 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX

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 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX
# Run inference directly in the terminal:
./llama-cli -hf 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX

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 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX

Use Docker

docker model run hf.co/1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX

LM Studio
Jan

vLLM

How to use 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-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": "1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX

Ollama

How to use 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF with Ollama:

ollama run hf.co/1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX

Unsloth Desktop

How to use 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX

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": "1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF with Docker Model Runner:
```
docker model run hf.co/1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX
```

Lemonade

How to use 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX

Run and chat with the model

lemonade run user.Qwen3.6-27B-Q8_0-ROCMFPX-GGUF-Q8_0_ROCMFPX

List all available models

lemonade list

Hermes Agent

How to use 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-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 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX

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 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX

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 "1337Hero/Qwen3.6-27B-Q8_0-ROCMFPX-GGUF:Q8_0_ROCMFPX" \
  --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-Q8_0-ROCMFPX-GGUF / ARTIFACT.json

1337Hero

Add model card and release metadata

5ad91c7 verified 3 months ago

Raw History Blame Contribute Delete

1.47 kB

	{
	"schema_version": 1,
	"artifact": {
	"filename": "Qwen3.6-27B-Q8_0_ROCMFPX.gguf",
	"bytes": 27755345216,
	"sha256": "ff4dbc9093c1df6fd1242294d15eb94c7bfe42ed67f98e9d58b9789ac6912c1b",
	"quantization": "Q8_0_ROCMFPX",
	"bits_per_weight": 8.25,
	"importance_matrix": false,
	"quantized_by": "1337Hero",
	"quantization_command": [
	"./build-r9700/bin/llama-quantize",
	"Qwen3.6-27B-BF16.gguf",
	"Qwen3.6-27B-Q8_0_ROCMFPX.gguf",
	"Q8_0_ROCMFPX",
	"16"
	]
	},
	"base_model": {
	"repo_id": "Qwen/Qwen3.6-27B",
	"license": "apache-2.0",
	"source_gguf_sha256": "0438be1f5bc861ffa84e1d2d4036920f6f3d9759f3cdedbc40e554a321d1c9c5"
	},
	"runtime": {
	"repository": "https://github.com/1337hero/ROCmFPX",
	"base_commit": "6bf20cd688ba0af882d1f68ba50b292edf646ab4",
	"implementation_commits": [
	"eb38c6f67701ff9c74e8597f573eedf9ccecf774",
	"45bcff509c4b1cff137e2cc1ea84671c61ceddea"
	],
	"llama_server_sha256": "1471753a94ba007b094842474cc3b3ffd48106f15a7c71934899dd832ae4cbb3",
	"llama_bench_sha256": "38bc30e53badc5ae5efb5e3d449d989a4672c52fe5801eb1ce74e70233283749",
	"llama_quantize_sha256": "0aa545af25e8235613349987fb29323745b70980aa2a4fb45c169cdf561181c6"
	},
	"validated_platform": {
	"gpu": "AMD Radeon AI PRO R9700",
	"architecture": "gfx1201",
	"rocm": "7.2.4",
	"host_os": "Arch Linux",
	"power_profile": "250 W, -75 mV"
	},
	"claims": "claims.yaml"
	}