Instructions to use Gorilla4X/Quacken-R1-14B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gorilla4X/Quacken-R1-14B-FP8 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Gorilla4X/Quacken-R1-14B-FP8",
	filename="DeepSeek-R1-Distill-Qwen-14B-Quark-F8E4M3.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 Gorilla4X/Quacken-R1-14B-FP8 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 Gorilla4X/Quacken-R1-14B-FP8
# Run inference directly in the terminal:
llama cli -hf Gorilla4X/Quacken-R1-14B-FP8

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Gorilla4X/Quacken-R1-14B-FP8
# Run inference directly in the terminal:
llama cli -hf Gorilla4X/Quacken-R1-14B-FP8

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 Gorilla4X/Quacken-R1-14B-FP8
# Run inference directly in the terminal:
./llama-cli -hf Gorilla4X/Quacken-R1-14B-FP8

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 Gorilla4X/Quacken-R1-14B-FP8
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Gorilla4X/Quacken-R1-14B-FP8

Use Docker

docker model run hf.co/Gorilla4X/Quacken-R1-14B-FP8

LM Studio
Jan

vLLM

How to use Gorilla4X/Quacken-R1-14B-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gorilla4X/Quacken-R1-14B-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gorilla4X/Quacken-R1-14B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Gorilla4X/Quacken-R1-14B-FP8

Ollama
How to use Gorilla4X/Quacken-R1-14B-FP8 with Ollama:
```
ollama run hf.co/Gorilla4X/Quacken-R1-14B-FP8
```

Unsloth Studio

How to use Gorilla4X/Quacken-R1-14B-FP8 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 Gorilla4X/Quacken-R1-14B-FP8 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 Gorilla4X/Quacken-R1-14B-FP8 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Gorilla4X/Quacken-R1-14B-FP8 to start chatting

Atomic Chat new
Docker Model Runner
How to use Gorilla4X/Quacken-R1-14B-FP8 with Docker Model Runner:
```
docker model run hf.co/Gorilla4X/Quacken-R1-14B-FP8
```

Lemonade

How to use Gorilla4X/Quacken-R1-14B-FP8 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Gorilla4X/Quacken-R1-14B-FP8

Run and chat with the model

lemonade run user.Quacken-R1-14B-FP8-{{QUANT_TAG}}

List all available models

lemonade list

Quacken-R1-14B-FP8

The Rock8 - Got any weights? 💪🦆

Native fp8 E4M3 GGUF of DeepSeek-R1-Distill-Qwen-14B (a reasoning model) for AMD RDNA4 (gfx1201 - Radeon AI PRO R9700 / RX 9070 / 9070 XT / W-series), quantized with AMD Quark from the full-precision BF16 weights by The Rock8.

The Rock8's llama.cpp fork runs this fp8 on RDNA4's native WMMA fp8 tensor cores (prefill) and v_dot4_f32_fp8_fp8 (decode) - not a dequant-to-f16 fallback.

What it is

Format: fp8 E4M3 (F8E4M3), block-scaled, produced by AMD Quark from BF16.
Target: AMD RDNA4 / gfx1201 (Radeon AI PRO R9700, RX 9070 / 9070 XT).
Runtime: The Rock8 (llama.cpp fork with native RDNA4 fp8 kernels) on TheRock ROCm 7.13.
File: DeepSeek-R1-Distill-Qwen-14B-Quark-F8E4M3.gguf (16 GB).

Reasoning model - usage note

This is an R1-distill reasoning model. It emits a chain-of-thought inside <think>...</think> before the final answer; llama.cpp / OpenAI-compatible servers surface that as reasoning_content. To disable thinking for a turn, append /no_think to the prompt (or set the chat template's thinking flag off). Expect longer generations by default because of the reasoning trace.

Source model + license

Source: unsloth/DeepSeek-R1-Distill-Qwen-14B (a mirror of deepseek-ai/DeepSeek-R1-Distill-Qwen-14B).
License: MIT (inherited from the source model; redistribution of this quantized derivative is permitted with attribution). This is a derivative work.

Validation (real gfx1201 hardware)

Metric	Value
Perplexity (wikitext, 20 chunks, n_ctx=512)	8.97
Prefill `pp512`	2499 t/s
Decode `tg128`	33.4 t/s

Benched on a single R9700 (gfx1201).

Run it

llama.cpp (The Rock8 fork)

# reasoning chat (keeps <think>)
llama-cli   -m DeepSeek-R1-Distill-Qwen-14B-Quark-F8E4M3.gguf -ngl 99 \
  -p "Solve step by step: a train travels 60 km in 40 minutes. What is its speed in km/h?"
# fast, no reasoning trace
llama-cli   -m DeepSeek-R1-Distill-Qwen-14B-Quark-F8E4M3.gguf -ngl 99 -p "What do you call a dried grape? Answer in one word. /no_think"
# bench
llama-bench -m DeepSeek-R1-Distill-Qwen-14B-Quark-F8E4M3.gguf -ngl 99 -p 512 -n 128

Lemonade appliance (container)

podman run -d --rm --runtime crun --name lemonade \
  --device /dev/kfd --device /dev/dri \
  --group-add keep-groups --security-opt seccomp=unconfined \
  -v /path/to/quacken-r1-14b:/models:ro \
  -e MODEL=/models/DeepSeek-R1-Distill-Qwen-14B-Quark-F8E4M3.gguf -e MODEL_NAME=Quacken-R1-14B-FP8 \
  -e HIP_VISIBLE_DEVICES=0 -p 13305:13305 \
  ghcr.io/the-monk/the-rock8:rdna4-tr713 serve

Container (same image on each registry; --runtime crun is required for GPU): ghcr.io/the-monk/the-rock8:rdna4-tr713 - docker.io/gorilla4x/the-rock8:rdna4-tr713 - quay.io/the-monk/the-rock8:rdna4-tr713 (images may not be pushed to every registry yet).

The Rock8 - RDNA4 fp8 (links)

GitHub: The-Rock8 - kernels, patch series, appliance recipe, full feature doc.
Collection: The Rock8 - RDNA4 fp8.
Sibling model: Quacken-8B-FP8.
3 more coming: Quacken-27B-FP8 (Qwen3.6-27B), Qwen3.6-35B-A3B (MoE) fp8, and Ornith-1.0-35B fp8 (in final validation).

Every artifact links to the others - land on any one, reach them all.