Instructions to use Gorilla4X/Quacken-Ornith-35B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Gorilla4X/Quacken-Ornith-35B-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-Ornith-35B-FP8
# Run inference directly in the terminal:
llama cli -hf Gorilla4X/Quacken-Ornith-35B-FP8

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Gorilla4X/Quacken-Ornith-35B-FP8
# Run inference directly in the terminal:
llama cli -hf Gorilla4X/Quacken-Ornith-35B-FP8

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 Gorilla4X/Quacken-Ornith-35B-FP8
# Run inference directly in the terminal:
./build/bin/llama cli -hf Gorilla4X/Quacken-Ornith-35B-FP8

Use Docker

docker model run hf.co/Gorilla4X/Quacken-Ornith-35B-FP8

Llama new
LM Studio
Jan

vLLM

How to use Gorilla4X/Quacken-Ornith-35B-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gorilla4X/Quacken-Ornith-35B-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-Ornith-35B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Gorilla4X/Quacken-Ornith-35B-FP8

Ollama
How to use Gorilla4X/Quacken-Ornith-35B-FP8 with Ollama:
```
ollama run hf.co/Gorilla4X/Quacken-Ornith-35B-FP8
```
Unsloth Desktop

How to use Gorilla4X/Quacken-Ornith-35B-FP8 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Gorilla4X/Quacken-Ornith-35B-FP8

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": "Gorilla4X/Quacken-Ornith-35B-FP8"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Gorilla4X/Quacken-Ornith-35B-FP8 with Docker Model Runner:
```
docker model run hf.co/Gorilla4X/Quacken-Ornith-35B-FP8
```

Lemonade

How to use Gorilla4X/Quacken-Ornith-35B-FP8 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Gorilla4X/Quacken-Ornith-35B-FP8

Run and chat with the model

lemonade run user.Quacken-Ornith-35B-FP8-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Gorilla4X/Quacken-Ornith-35B-FP8 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Gorilla4X/Quacken-Ornith-35B-FP8

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 Gorilla4X/Quacken-Ornith-35B-FP8

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Gorilla4X/Quacken-Ornith-35B-FP8 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Gorilla4X/Quacken-Ornith-35B-FP8

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 "Gorilla4X/Quacken-Ornith-35B-FP8" \
  --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"

Quacken-Ornith-35B-FP8 / README.md

Gorilla4X

card: RDNA4 native-fp8 (Quark) -- The Rock8

9669a85 verified 4 months ago

preview code

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4.08 kB

metadata

license: apache-2.0
base_model: Ornith-1.0-35B
base_model_relation: quantized
pipeline_tag: text-generation
library_name: gguf
tags:
  - the-rock8
  - rock8
  - rdna4
  - gfx1201
  - radeon-ai-pro-r9700
  - rx-9070
  - fp8
  - f8e4m3
  - quark
  - gguf
  - amd
  - rocm
  - llama.cpp
  - quantized
  - moe
  - mixture-of-experts
model_name: Quacken-Ornith-35B-FP8
quantized_by: The Rock8

Quacken-Ornith-35B-FP8

The Rock8 - Got any weights? 💪🦆

Native fp8 E4M3 GGUF of Ornith-1.0-35B - a Mixture-of-Experts GatedDeltaNet-hybrid (multimodal-capable) 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 - no dequant-to-f16 fallback.

What it is

Format: fp8 E4M3 (F8E4M3), block-scaled, produced by AMD Quark from BF16.
Architecture: MoE + GatedDeltaNet SSM-hybrid. No MTP head (the checkpoint ships zero MTP tensors) - so self-speculative --spec-type draft-mtp is not available for this model; run it as a plain target.
Target: AMD RDNA4 / gfx1201; ~38 GB -> runs 2-GPU (tensor-split across two 32 GB R9700s) or one large card.
Runtime: The Rock8 (llama.cpp fork with native RDNA4 fp8 kernels) on TheRock ROCm 7.13.
File: ornith-1.0-35b-quark-fp8-authentic.gguf (37.8 GB).

Important run note

This is an SSM-hybrid architecture. Run it with llama-server or llama-perplexity - not llama-bench (llama-bench cannot allocate the SSM-hybrid context and will fail on this arch). That is a tooling limitation, not a model defect; the model is validated and generates correctly under llama-server.

Source model + license

Source: Ornith-1.0-35B.
License: Apache-2.0 (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), 2-GPU	6.7010

Validated via llama-perplexity on dual R9700 (gfx1201).

Run it (2-GPU)

# use llama-server (NOT llama-bench); -ngl 999 tensor-splits across both R9700s
llama-server -m ornith-1.0-35b-quark-fp8-authentic.gguf -ngl 999 --host 0.0.0.0 --port 13305

curl -s http://localhost:13305/v1/chat/completions -H 'Content-Type: application/json' \
  -d '{"messages":[{"role":"user","content":"What do you call a dried grape? Answer in one word."}],"max_tokens":16}'
# expect: raisin

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-ornith-35b:/models:ro \
  -e MODEL=/models/ornith-1.0-35b-quark-fp8-authentic.gguf -e MODEL_NAME=Quacken-Ornith-35B-FP8 \
  -p 13305:13305 \
  ghcr.io/the-monk/the-rock8:rdna4-tr713 serve
# ~38 GB - needs both GPUs; do NOT pin HIP_VISIBLE_DEVICES to a single card

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 models: Quacken-8B-FP8 - Quacken-R1-14B-FP8 - Quacken-27B-FP8 - Quacken-35B-A3B-FP8.

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