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.

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="paragon-of-brah/Ornith-1.0-397B-DFLASH-GGUF",
	filename="DFlash/Ornith-DFLASH-bf16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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
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

Ornith-1.0-397B-DFLASH-GGUF / DFlash /Ornith-DFLASH-q8.gguf

paragon-of-brah

Upload Ornith-DFLASH-q8.gguf

bbc3e49 verified 4 days ago

Download

History Blame GGUF Editor new Delete

1.38 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 6677cd862cd337de70da27384d20c0433f6ccaf773977eccdd55a6916379d9ee
Size of remote file:: 1.38 GB
SHA256:: dd094dade270bcf1046392bd14c43bc275ee2b62508af2ef0c95d51bc4ebceea

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.