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

Libraries

How to use philtheriver/Qwen3.6-27B-ROCmFPX with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="philtheriver/Qwen3.6-27B-ROCmFPX",
	filename="Qwen3.6-27B-STRIX-embF16-Q3_0_ROCMFPX.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 philtheriver/Qwen3.6-27B-ROCmFPX 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 philtheriver/Qwen3.6-27B-ROCmFPX:BF16
# Run inference directly in the terminal:
llama cli -hf philtheriver/Qwen3.6-27B-ROCmFPX:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf philtheriver/Qwen3.6-27B-ROCmFPX:BF16
# Run inference directly in the terminal:
llama cli -hf philtheriver/Qwen3.6-27B-ROCmFPX:BF16

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 philtheriver/Qwen3.6-27B-ROCmFPX:BF16
# Run inference directly in the terminal:
./llama-cli -hf philtheriver/Qwen3.6-27B-ROCmFPX:BF16

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 philtheriver/Qwen3.6-27B-ROCmFPX:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf philtheriver/Qwen3.6-27B-ROCmFPX:BF16

Use Docker

docker model run hf.co/philtheriver/Qwen3.6-27B-ROCmFPX:BF16

LM Studio
Jan

vLLM

How to use philtheriver/Qwen3.6-27B-ROCmFPX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "philtheriver/Qwen3.6-27B-ROCmFPX"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "philtheriver/Qwen3.6-27B-ROCmFPX",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/philtheriver/Qwen3.6-27B-ROCmFPX:BF16

Ollama
How to use philtheriver/Qwen3.6-27B-ROCmFPX with Ollama:
```
ollama run hf.co/philtheriver/Qwen3.6-27B-ROCmFPX:BF16
```

Unsloth Studio

How to use philtheriver/Qwen3.6-27B-ROCmFPX 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 philtheriver/Qwen3.6-27B-ROCmFPX 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 philtheriver/Qwen3.6-27B-ROCmFPX to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for philtheriver/Qwen3.6-27B-ROCmFPX to start chatting

How to use philtheriver/Qwen3.6-27B-ROCmFPX with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf philtheriver/Qwen3.6-27B-ROCmFPX:BF16

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": "philtheriver/Qwen3.6-27B-ROCmFPX:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use philtheriver/Qwen3.6-27B-ROCmFPX with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf philtheriver/Qwen3.6-27B-ROCmFPX:BF16

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 philtheriver/Qwen3.6-27B-ROCmFPX:BF16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use philtheriver/Qwen3.6-27B-ROCmFPX with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf philtheriver/Qwen3.6-27B-ROCmFPX:BF16

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 "philtheriver/Qwen3.6-27B-ROCmFPX:BF16" \
  --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 philtheriver/Qwen3.6-27B-ROCmFPX with Docker Model Runner:
```
docker model run hf.co/philtheriver/Qwen3.6-27B-ROCmFPX:BF16
```

Lemonade

How to use philtheriver/Qwen3.6-27B-ROCmFPX with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull philtheriver/Qwen3.6-27B-ROCmFPX:BF16

Run and chat with the model

lemonade run user.Qwen3.6-27B-ROCmFPX-BF16

List all available models

lemonade list

Qwen3.6-27B-ROCmFPX / README.md

philtheriver

Upload README.md with huggingface_hub

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	---
	license: apache-2.0
	base_model: unsloth/Qwen3.6-27B-MTP-GGUF
	base_model_relation: quantized
	pipeline_tag: text-generation
	library_name: llama.cpp
	language:
	- en
	tags:
	- gguf
	- rocmfpx
	- rocm
	- amd
	- strix-halo
	- gfx1151
	- mtp
	- qwen3
	- vision
	---

	# Qwen3.6-27B · ROCmFPX

	### Stock `Q6_K` quality, ~30% faster prompt-processing on AMD Strix Halo (`gfx1151`)

	ROCmFPX 3→8-bit quants of [`unsloth/Qwen3.6-27B-MTP-GGUF`](https://huggingface.co/unsloth/Qwen3.6-27B-MTP-GGUF) — the general-purpose base, with MTP speculative decoding + Qwen3-VL vision. Neutral imatrix calibration, so base behavior is preserved (not skewed toward code).

	\| \| \|
	\|---\|---\|
	\| Quality \| ≈ stock `Q6_K` (PPL within error) \|
	\| Prompt processing \| +29% vs Q6_K (short ctx) → +20% at 64k — measured \|
	\| Decode \| \~18 tok/s with MTP (~9 raw) \|
	\| Vision \| Qwen3-VL — bundled `mmproj/` \|

	> ⚠️ Requires the [ROCmFPX fork](https://github.com/charlie12345/ROCmFPX) (build `main` — the FP* types are merged in) — custom AMD quant types (enum IDs 110–115), not upstream-stable. Won't load in stock llama.cpp / LM Studio / Ollama. HF's precision badge is wrong — pick the file by name.

	## Pick a tier

	\| File suffix \| Size \| Best for \|
	\|---\|---\|---\|
	\| `…embF16-headQ6-Q6_0_ROCMFPX.gguf` ★ \| 23G \| best overall — the flagship \|
	\| `…embF16-Q8_0_ROCMFPX.gguf` \| 28G \| maximum fidelity \|
	\| `…embF16-Q4_0_ROCMFP4.gguf` \| 19G \| fastest decode (4-bit) \|
	\| `…embF16-Q3_0_ROCMFPX.gguf` \| 16G \| smallest \|

	All filenames prefixed `Qwen3.6-27B-STRIX-`.

	## Quick start

	```bash
	# build the fork once — main already has the ROCmFPX quant types
	git clone https://github.com/charlie12345/ROCmFPX.git && cd ROCmFPX
	JOBS=16 scripts/build-strix-rocmfp4-mtp.sh

	# serve the flagship — MTP + vision
	HSA_OVERRIDE_GFX_VERSION=11.5.1 build-strix-rocmfp4/bin/llama-server \
	-m Qwen3.6-27B-STRIX-embF16-headQ6-Q6_0_ROCMFPX.gguf \
	-dev ROCm0 -ngl 999 -fa on -c 32768 \
	--spec-type draft-mtp --spec-draft-ngl all --spec-draft-n-max 2 \
	--jinja --mmproj mmproj/mmproj-F32.gguf --host 0.0.0.0 --port 8080
	```

	Tool calls: serve with `--jinja` so the model's own chat template emits them — no special parser flag needed.

	<details>
	<summary><b>All tiers · recipe · benchmarks</b></summary>

	### All tiers

	\| File suffix \| Preset \| Enum \| Size \| Role \|
	\|---\|---\|---\|---\|---\|
	\| `embF16-headQ6-Q6_0_ROCMFPX.gguf` \| `Q6_0_ROCMFPX` \| 110 \| 23G \| flagship — f16 emb + Q6_K head + imatrix \|
	\| `embF16-Q8_0_ROCMFPX.gguf` \| `Q8_0_ROCMFPX` \| 111 \| 28G \| highest fidelity \|
	\| `embF16-Q4_0_ROCMFP4.gguf` \| `Q4_0_ROCMFP4` \| 100 \| 19G \| fastest decode (4-bit body) \|
	\| `embF16-Q3_0_ROCMFPX.gguf` \| `Q3_0_ROCMFPX` \| 112 \| 16G \| smallest \|

	f16 token embeddings throughout. (HF labels `Q4`/`Q8` but not `Q6`/`Q3` — the latter aren't standard llama.cpp quant names; pick by filename.)

	### Verification (Strix Halo gfx1151)

	\| Metric \| Value \|
	\|---\|---\|
	\| Functional smoke \| chat/coding/JSON/tool-call/coherency ✅ (5/5) \|
	\| PPL vs `Q6_K` \| flagship 5.7030 vs stock `Q6_K` 5.6618 → Δ +0.73% (general slice, ctx 512) \|

	### Performance — prompt-processing throughput (t/s) vs `Q6_K`

	\| Context \| `Q6_K` \| flagship \| Δ \|
	\|---\|---\|---\|---\|
	\| pp512 \| 188 \| 242 \| +29% \|
	\| pp2048 \| 191 \| 245 \| +28% \|
	\| pp10k \| 180 \| 228 \| +27% \|
	\| pp16k \| 172 \| 217 \| +26% \|
	\| pp32k \| 156 \| 193 \| +23% \|
	\| pp64k \| 133 \| 159 \| +20% \|

	Clean dedicated-GPU `llama-bench` (both servers stopped, warm-up pass discarded). The gfx1151-tuned ROCmFPX kernels win the compute-bound prefill — the edge is largest at short context and narrows to +20% at 64k as O(n²) attention takes over. Decode is bandwidth-bound (≈ Q6_K raw), and MTP (`--spec-type draft-mtp`) ~doubles it in serving. Single-rep `llama-bench`; treat absolutes as ±a few %.

	</details>

	## Credits & license

	Apache-2.0 (inherited). Qwen3.6 (base) → [charlie12345 / ROCmFPX](https://github.com/charlie12345/ROCmFPX) (quant fork). ROCmFPX quantization by this repo's author.