Text Generation
GGUF
llama.cpp
pocketpal
on-device
Mixture of Experts
multilingual
quantization
distillation
1-bit
2-bit
3-bit
conversational
Instructions to use RegalInvader/Marco-Nano-Instruct-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 RegalInvader/Marco-Nano-Instruct-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 RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0 # Run inference directly in the terminal: llama cli -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0 # Run inference directly in the terminal: llama cli -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_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 RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
Use Docker
docker model run hf.co/RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
- Llama new
- LM Studio
- Jan
- vLLM
How to use RegalInvader/Marco-Nano-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RegalInvader/Marco-Nano-Instruct-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": "RegalInvader/Marco-Nano-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
- Ollama
How to use RegalInvader/Marco-Nano-Instruct-GGUF with Ollama:
ollama run hf.co/RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
- Unsloth Desktop
- Pi
How to use RegalInvader/Marco-Nano-Instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_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": "RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use RegalInvader/Marco-Nano-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
- Lemonade
How to use RegalInvader/Marco-Nano-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
Run and chat with the model
lemonade run user.Marco-Nano-Instruct-GGUF-TQ1_0
List all available models
lemonade list
- Hermes Agent
How to use RegalInvader/Marco-Nano-Instruct-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 RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_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 RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use RegalInvader/Marco-Nano-Instruct-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_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 "RegalInvader/Marco-Nano-Instruct-GGUF:TQ1_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 code/scripts/rs_retention.py from RegalInvader/Marco-Nano-Instruct-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 1.92 kB
-
https://huggingface.co/RegalInvader/Marco-Nano-Instruct-GGUF/resolve/main/code/scripts/rs_retention.py
- Command line
-
hf download hf://RegalInvader/Marco-Nano-Instruct-GGUF/code/scripts/rs_retention.py
-
curl -L -o rs_retention.py https://huggingface.co/RegalInvader/Marco-Nano-Instruct-GGUF/resolve/main/code/scripts/rs_retention.py
1.92 kB
| #!/usr/bin/env python3 | |
| """Retention of a student against the teacher on an RS suite (spec §7): R = s / t and R_c = (s − c) / (t − c) per | |
| task, with each task's metric and chance level from the suite file. | |
| python scripts/rs_retention.py --suite configs/eval/rs_fast.yaml \ | |
| --teacher $MT_DATA/evals/teacher/marco-nano-instruct/rs_fast/*/summary.json \ | |
| --student $MT_DATA/runs/<run>/rs_fast/*/summary.json [--markdown] | |
| """ | |
| from __future__ import annotations | |
| import argparse | |
| import json | |
| import yaml | |
| def scores(paths: list[str]) -> dict[str, float]: | |
| out: dict[str, float] = {} | |
| for p in paths: | |
| out.update(json.load(open(p))["scores"]) | |
| return out | |
| def main() -> None: | |
| p = argparse.ArgumentParser() | |
| p.add_argument("--suite", required=True) | |
| p.add_argument("--teacher", nargs="+", required=True) | |
| p.add_argument("--student", nargs="+", required=True) | |
| p.add_argument("--markdown", action="store_true") | |
| a = p.parse_args() | |
| suite = yaml.safe_load(open(a.suite)) | |
| chance = suite.get("chance", {}) | |
| t, s = scores(a.teacher), scores(a.student) | |
| rows = [] | |
| for task in sorted(set(t) & set(s)): | |
| c = chance.get(task, chance.get("default", 0.0)) | |
| rows.append({"task": task, "teacher": t[task], "student": s[task], "R": s[task] / t[task] if t[task] else None, | |
| "R_c": (s[task] - c) / (t[task] - c) if t[task] != c else None}) | |
| missing = sorted(set(t) ^ set(s)) | |
| if a.markdown: | |
| print("| task | teacher | student | R | R_c |\n|------|---------|---------|---|-----|") | |
| for r in rows: | |
| print(f"| {r['task']} | {r['teacher']:.3f} | {r['student']:.3f} | {r['R']:.3f} | {r['R_c']:.3f} |") | |
| if missing: | |
| print(f"\nNot in both: {', '.join(missing)}") | |
| else: | |
| print(json.dumps({"rows": rows, "missing": missing}, indent=1)) | |
| if __name__ == "__main__": | |
| main() | |