Instructions to use Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Blackfrost-AI/Qwythos-9B-V2-EMERGED-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 Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_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 Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_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 Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_0

Use Docker

docker model run hf.co/Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Blackfrost-AI/Qwythos-9B-V2-EMERGED-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": "Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_0

Ollama
How to use Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF with Ollama:
```
ollama run hf.co/Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_0
```

Unsloth Studio

How to use Blackfrost-AI/Qwythos-9B-V2-EMERGED-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 Blackfrost-AI/Qwythos-9B-V2-EMERGED-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 Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF to start chatting

How to use Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_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": "Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Blackfrost-AI/Qwythos-9B-V2-EMERGED-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 Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_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 Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_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 "Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_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 Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF with Docker Model Runner:
```
docker model run hf.co/Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_0
```

Lemonade

How to use Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Blackfrost-AI/Qwythos-9B-V2-EMERGED-GGUF:Q8_0

Run and chat with the model

lemonade run user.Qwythos-9B-V2-EMERGED-GGUF-Q8_0

List all available models

lemonade list

Qwythos-9B-EMERGED — GGUF

GGUF build of Blackfrost-AI/Qwythos-9B-V2-EMERGED-BF16 — the pure-reasoning / agentic distillation of the Qwythos 9B base, trained by Blackfrost AI on The Void. This repository packages those weights in the GGUF format for llama.cpp, Ollama, and LM Studio.

See the BF16 model card for the full model description, training method, and safety posture. This card documents only the GGUF conversion. The architecture, capabilities, limitations, and responsible-use guidance are identical to the source model and are not repeated here.

What this is


Source model	`Blackfrost-AI/Qwythos-9B-V2-EMERGED-BF16`
Format	GGUF (quantized for CPU/edge inference runtimes)
Base family	`qwen3_5` (Qwen3.6-class) dense 9B, ~9.5B params
Modality	Vision-language — text + image + video in, text out (vision tower inherited from base; see source card)
Precision	quantized (see per-file list below)
Context	1,048,576 (1M) native, inherited from the base

This is a format conversion of the BF16 weights — no retraining, no modification of the underlying model. All behavioral characteristics (including limitations and the inherited vision tower) are identical to the source model.

Run (llama.cpp)

# one-shot
llama-cli -m qwythos-9b-emerged.<QUANT>.gguf -p "Explain why a hash table has O(1) average lookup."

# OpenAI-compatible server
llama-server -m qwythos-9b-emerged.<QUANT>.gguf --port 8000

Ollama: ollama create qwythos-9b-emerged -f Modelfile (point FROM at the local GGUF file).

For multimodal (image/video) input, use a runtime with the source model's vision processor. The GGUF packaging is intended primarily for text inference; validate multimodal behavior against the BF16 source for your use case.

Responsible use

This is an uncensored reasoning model — refusal behavior was removed during distillation. You are responsible for how you use it and for any safety layer you place around it. Do not use it to produce content that is illegal in your jurisdiction. See the source model card for the full safety posture and limitations.

License

Apache-2.0, inherited from the base model lineage (empero-ai/Qwythos-9B-v2).

Provenance

Source model: Blackfrost-AI/Qwythos-9B-V2-EMERGED-BF16 (Blackfrost AI — The Void distillation)
Base model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M (empero-ai, Apache-2.0)
Format tooling: llama.cpp GGUF conversion utilities

Trained and released by Blackfrost AI on The Void. Base model by empero-ai (Apache-2.0).