Instructions to use paragon-of-brah/Ornith-1.0-397B-DFLASH-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 paragon-of-brah/Ornith-1.0-397B-DFLASH-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 paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16

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 paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16

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 paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16

Use Docker

docker model run hf.co/paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16

LM Studio
Jan
Ollama
How to use paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF with Ollama:
```
ollama run hf.co/paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16
```

Unsloth Studio

How to use paragon-of-brah/Ornith-1.0-397B-DFLASH-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 paragon-of-brah/Ornith-1.0-397B-DFLASH-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 paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF to start chatting

How to use paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16

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": "paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use paragon-of-brah/Ornith-1.0-397B-DFLASH-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 paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16

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 paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16

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 "paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16" \
  --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 paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF with Docker Model Runner:
```
docker model run hf.co/paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16
```

Lemonade

How to use paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF:BF16

Run and chat with the model

lemonade run user.Ornith-1.0-397B-DFLASH-GGUF-BF16

List all available models

lemonade list

how Q2_K so small

by CHHORVORN - opened Jun 28

Discussion

CHHORVORN

Jun 28

your Q2_K 79.6 GB and bartowski Q2_K 139 GB that's almost half compare to bartowski

paragon-of-brah

Owner Jun 28

Well, the last shard is still uploading

that said, my model is IQ2_KS, not Q2_K. hugging face doesn't handle ik_llama quant types well, so it displays the wrong type.

paragon-of-brah

Owner Jun 28

Ok, all shards have been uploaded

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment