Instructions to use petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF",
	filename="ornith-1.0-35b-MTP-graft-down-Q4_0.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 petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-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 petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0

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 petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0

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 petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0

Use Docker

docker model run hf.co/petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-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": "petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0

Ollama
How to use petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF with Ollama:
```
ollama run hf.co/petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0
```

Unsloth Studio

How to use petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF 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 petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF 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 petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF to start chatting

How to use petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0

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": "petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-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 petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0

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 petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0

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 "petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0" \
  --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 petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF with Docker Model Runner:
```
docker model run hf.co/petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0
```

Lemonade

How to use petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF:Q4_0

Run and chat with the model

lemonade run user.Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF-Q4_0

List all available models

lemonade list

petr567 commited on 3 days ago

Commit

38bb75c

verified ·

1 Parent(s): 9e3a41c

docs: verify clean Windows download and endpoint recipe

Browse files

Files changed (2) hide show

README.md +5 -0
WINDOWS_ENDPOINT_QUICKSTART.md +23 -5

README.md CHANGED Viewed

@@ -47,6 +47,11 @@ the measured CPU-MoE/GPU-dense MTP profile.
 Endpoint: `http://127.0.0.1:18081/v1`. Do not substitute the no-MTP LM Studio
 file if speculative acceleration is required.
 This repository contains a single, deployment-ready GGUF optimized and tested
 for batch-1 agent workloads on AMD Ryzen AI MAX+ 395 / Radeon 8060S (`gfx1151`,
 128 GiB UMA) with the Vulkan backend of llama.cpp b9994. The same artifact was

 Endpoint: `http://127.0.0.1:18081/v1`. Do not substitute the no-MTP LM Studio
 file if speculative acceleration is required.
+The documented flow was re-tested from a fresh `C:\Models\Ornith-MTP`
+directory using a real Hugging Face download (no hard links), SHA-256
+verification, Docker startup, `/health`, `/v1/models`, and
+`/v1/chat/completions`.
 This repository contains a single, deployment-ready GGUF optimized and tested
 for batch-1 agent workloads on AMD Ryzen AI MAX+ 395 / Radeon 8060S (`gfx1151`,
 128 GiB UMA) with the Vulkan backend of llama.cpp b9994. The same artifact was

WINDOWS_ENDPOINT_QUICKSTART.md CHANGED Viewed

@@ -34,26 +34,45 @@ weights in host RAM.
 ## 2. Download the full MTP artifact
-Create a model directory and download this repository's GGUF:
 ```powershell
-py -m pip install --upgrade "huggingface_hub[cli]"
 New-Item -ItemType Directory -Force C:\Models\Ornith-MTP | Out-Null
-hf download `
   petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF `
   ornith-1.0-35b-MTP-graft-down-Q4_0.gguf `
   --local-dir C:\Models\Ornith-MTP
 ```
 Expected file:
 ```text
 C:\Models\Ornith-MTP\ornith-1.0-35b-MTP-graft-down-Q4_0.gguf
 SHA-256: 365a7c02dfd320b9696f189d6dc12bd2b0eabb9f8e58ba9fc8cab3af93c0234b
 ```
 Verify it:
 ```powershell
@@ -227,4 +246,3 @@ docker run --rm `
 ```
 It must report llama.cpp version 10066 (`86a9c79f8`) for this exact recipe.

 ## 2. Download the full MTP artifact
+Create a model directory and an isolated virtual environment for the Hugging
+Face CLI. Do not upgrade `huggingface_hub` in a shared Python installation:
+new CLI releases can require a newer `click` than packages such as `gTTS`.
 ```powershell
 New-Item -ItemType Directory -Force C:\Models\Ornith-MTP | Out-Null
+$HfVenv = 'C:\Models\Ornith-MTP\.hf-cli'
+py -m venv $HfVenv
+& "$HfVenv\Scripts\python.exe" -m pip install --upgrade pip
+& "$HfVenv\Scripts\python.exe" -m pip install "huggingface_hub[hf_xet]==1.24.0"
+& "$HfVenv\Scripts\hf.exe" download `
   petr567/Ornith-1.0-35B-MTP-Strix-Halo-Hybrid-GGUF `
   ornith-1.0-35b-MTP-graft-down-Q4_0.gguf `
   --local-dir C:\Models\Ornith-MTP
 ```
+The download is approximately 19.4 GiB. Xet may spend time scanning chunks
+without continuously printing progress; leave the command running until the
+PowerShell prompt returns. A partially downloaded file is resumed on the next
+identical command.
+This creates a real standalone copy downloaded from Hugging Face. The recipe
+does not use a hard link, symbolic link, or an already installed LM Studio
+model.
 Expected file:
 ```text
 C:\Models\Ornith-MTP\ornith-1.0-35b-MTP-graft-down-Q4_0.gguf
+Size: 20,329,342,112 bytes (18.933 GiB)
 SHA-256: 365a7c02dfd320b9696f189d6dc12bd2b0eabb9f8e58ba9fc8cab3af93c0234b
 ```
+This clean download and endpoint flow was re-tested on Windows on 19 July
+2026, including checksum verification, Docker startup, `/health`, `/v1/models`,
+and `/v1/chat/completions`.
 Verify it:
 ```powershell
 ```
 It must report llama.cpp version 10066 (`86a9c79f8`) for this exact recipe.