Text Classification
GGUF
lora
classifier
zero-shot-classification
calibrated-probabilities
fermi
conversational
Instructions to use Zerflyne/FERMI-0.8B 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 Zerflyne/FERMI-0.8B 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 Zerflyne/FERMI-0.8B:Q8_0 # Run inference directly in the terminal: llama cli -hf Zerflyne/FERMI-0.8B:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Zerflyne/FERMI-0.8B:Q8_0 # Run inference directly in the terminal: llama cli -hf Zerflyne/FERMI-0.8B:Q8_0
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Zerflyne/FERMI-0.8B:Q8_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf Zerflyne/FERMI-0.8B:Q8_0
Use Docker
docker model run hf.co/Zerflyne/FERMI-0.8B:Q8_0
- Llama new
- LM Studio
- Jan
- Ollama
How to use Zerflyne/FERMI-0.8B with Ollama:
ollama run hf.co/Zerflyne/FERMI-0.8B:Q8_0
- Unsloth Desktop
- Pi
How to use Zerflyne/FERMI-0.8B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Zerflyne/FERMI-0.8B:Q8_0
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": "Zerflyne/FERMI-0.8B:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Zerflyne/FERMI-0.8B with Docker Model Runner:
docker model run hf.co/Zerflyne/FERMI-0.8B:Q8_0
- Lemonade
How to use Zerflyne/FERMI-0.8B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Zerflyne/FERMI-0.8B:Q8_0
Run and chat with the model
lemonade run user.FERMI-0.8B-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use Zerflyne/FERMI-0.8B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Zerflyne/FERMI-0.8B: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 Zerflyne/FERMI-0.8B:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Zerflyne/FERMI-0.8B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Zerflyne/FERMI-0.8B: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 "Zerflyne/FERMI-0.8B: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"
Download examples/quickstart.py from Zerflyne/FERMI-0.8B: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
-
https://huggingface.co/Zerflyne/FERMI-0.8B/resolve/main/examples/quickstart.py
- Command line
-
hf download hf://Zerflyne/FERMI-0.8B/examples/quickstart.py
-
curl -L -o quickstart.py https://huggingface.co/Zerflyne/FERMI-0.8B/resolve/main/examples/quickstart.py
1.25 kB
| """FERMI in five lines: python examples/quickstart.py (add --device cpu to force the CPU)""" | |
| import json | |
| import os | |
| import sys | |
| sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | |
| from fermi import Fermi # noqa: E402 | |
| device = sys.argv[sys.argv.index("--device") + 1] if "--device" in sys.argv else "auto" | |
| m = Fermi.load(device=device) | |
| state = { | |
| "from": "customer@example.com", | |
| "subject": "Order arrived broken", | |
| "body": "The lamp I ordered arrived with the glass shattered. I'd like a replacement, not a refund. Photos attached.", | |
| } | |
| questions = { | |
| "wants_refund": {"type": "noul", "instructions": "Does the customer ask for a refund?", | |
| "criteria": {"true": "Asks for money back", "false": "Does not ask for money back"}}, | |
| "request": {"type": "choice", "instructions": "What does the customer want?", | |
| "criteria": {"replacement": "A new item", "refund": "Money back", "information": "An answer or explanation", | |
| "other": "Something else"}}, | |
| "urgency": {"type": "score", "instructions": "How urgent is the case?", "criteria": ["Low", "Medium", "High"]}, | |
| } | |
| print(json.dumps(m.classify(state, questions), indent=1, ensure_ascii=False)) | |