Instructions to use DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF",
	filename="Qwythos-9B-v2-Q5_K_L-imat-GGUF.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-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 DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF
# Run inference directly in the terminal:
llama cli -hf DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF
# Run inference directly in the terminal:
llama cli -hf DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF

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 DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF
# Run inference directly in the terminal:
./llama-cli -hf DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF

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 DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF

Use Docker

docker model run hf.co/DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF

LM Studio
Jan

vLLM

How to use DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-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": "DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF

Ollama
How to use DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF with Ollama:
```
ollama run hf.co/DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF
```

Unsloth Studio

How to use DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-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 DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-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 DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF to start chatting

How to use DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF

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": "DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-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 DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF

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 DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF

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 "DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF" \
  --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 DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF with Docker Model Runner:
```
docker model run hf.co/DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF
```

Lemonade

How to use DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DarkKitsune/Qwythos-9B-v2-Q5_K_L-imat-GGUF

Run and chat with the model

lemonade run user.Qwythos-9B-v2-Q5_K_L-imat-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

DarkKitsune commited on 7 days ago

Commit

85d3a6e

verified ·

1 Parent(s): 3f6b0e8

Update README.md

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@@ -26,7 +26,7 @@ language:
 GGUF quantization of `empero-ai/Qwythos-9B-v2` to Q5_K_L with importance matrices. Quantized using Q8_0 for token embedding and output weights, BF16 for ssm_alpha and ssm_beta weights, and the default llama.cpp Q5_K_M config for all other weights.
-Provides a bit higher precision than Q5_K_M, to help with more complex/long contexts and accuracy in edge cases.
 Be warned, the calibration file is mostly English text and code, so performance writing in other languages may suffer.

 GGUF quantization of `empero-ai/Qwythos-9B-v2` to Q5_K_L with importance matrices. Quantized using Q8_0 for token embedding and output weights, BF16 for ssm_alpha and ssm_beta weights, and the default llama.cpp Q5_K_M config for all other weights.
+Provides higher precision than Q5_K_M , and from test coding a few small projects, this quant appears to remain quite stable over many turns with long reasoning chains.
 Be warned, the calibration file is mostly English text and code, so performance writing in other languages may suffer.