Instructions to use 0xKitkat/Agnes-3.0-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use 0xKitkat/Agnes-3.0-Flash-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 0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf 0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf 0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf 0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M
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 0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf 0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M
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 0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf 0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M
Use Docker
docker model run hf.co/0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use 0xKitkat/Agnes-3.0-Flash-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "0xKitkat/Agnes-3.0-Flash-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": "0xKitkat/Agnes-3.0-Flash-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/0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M
- Ollama
How to use 0xKitkat/Agnes-3.0-Flash-GGUF with Ollama:
ollama run hf.co/0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use 0xKitkat/Agnes-3.0-Flash-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf 0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use 0xKitkat/Agnes-3.0-Flash-GGUF with Docker Model Runner:
docker model run hf.co/0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M
- Lemonade
How to use 0xKitkat/Agnes-3.0-Flash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull 0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Agnes-3.0-Flash-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use 0xKitkat/Agnes-3.0-Flash-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 0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M
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 0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use 0xKitkat/Agnes-3.0-Flash-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf 0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M
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 "0xKitkat/Agnes-3.0-Flash-GGUF:Q4_K_M" \ --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"
Download build/model_card_usage.py from 0xKitkat/Agnes-3.0-Flash-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 1.37 kB
-
https://huggingface.co/0xKitkat/Agnes-3.0-Flash-GGUF/resolve/9eae3a3f7527754ff19ceb80d862b15565e67c1e/build/model_card_usage.py
- Command line
-
hf download hf://0xKitkat/Agnes-3.0-Flash-GGUF@9eae3a3f7527754ff19ceb80d862b15565e67c1e/build/model_card_usage.py
-
curl -L -o model_card_usage.py https://huggingface.co/0xKitkat/Agnes-3.0-Flash-GGUF/resolve/9eae3a3f7527754ff19ceb80d862b15565e67c1e/build/model_card_usage.py
1.37 kB
| """Shared, editable GGUF usage guide for all release cards.""" | |
| from pathlib import Path | |
| def enhance_gguf_card(card, repo, model, llama_revision): | |
| usage = (Path(__file__).parent / "GGUF-USAGE.md").read_text(encoding="utf-8") | |
| for key, value in {"__REPO__": repo, "__MODEL__": model, "__LLAMA_REV__": llama_revision}.items(): | |
| usage = usage.replace(key, value) | |
| begin, end = "<!-- GGUF-USAGE-BEGIN -->", "<!-- GGUF-USAGE-END -->" | |
| section = begin + "\n" + usage.rstrip() + "\n" + end + "\n\n" | |
| if begin in card: | |
| start = card.index(begin) | |
| stop = card.index(end, start) + len(end) | |
| return card[:start] + section.rstrip() + card[stop:] | |
| # Replace the old short run recipe with the full guide before evaluations. | |
| if "\n## Run\n" in card: | |
| start = card.index("\n## Run\n") | |
| stops = [card.find(marker, start) for marker in ("\nOriginal model and implementation:", "\n## Attribution")] | |
| stops = [stop for stop in stops if stop >= 0] | |
| if not stops: | |
| raise ValueError("Cannot locate end of old GGUF run instructions") | |
| card = card[:start] + "\n" + card[min(stops):] | |
| for marker in ("## Validation\n", "## Measured checks\n"): | |
| if marker in card: | |
| return card.replace(marker, section + marker, 1) | |
| raise ValueError("Cannot locate GGUF evaluation section") | |