Instructions to use Raidone/raid-models-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Raidone/raid-models-base with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Raidone/raid-models-base",
	filename="raid-base-model.q4_k_m.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 Raidone/raid-models-base 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 Raidone/raid-models-base:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Raidone/raid-models-base:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Raidone/raid-models-base:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Raidone/raid-models-base: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 Raidone/raid-models-base:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Raidone/raid-models-base: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 Raidone/raid-models-base:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Raidone/raid-models-base:Q4_K_M

Use Docker

docker model run hf.co/Raidone/raid-models-base:Q4_K_M

LM Studio
Jan
Ollama
How to use Raidone/raid-models-base with Ollama:
```
ollama run hf.co/Raidone/raid-models-base:Q4_K_M
```

Unsloth Studio

How to use Raidone/raid-models-base 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 Raidone/raid-models-base 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 Raidone/raid-models-base to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Raidone/raid-models-base to start chatting

How to use Raidone/raid-models-base with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Raidone/raid-models-base:Q4_K_M

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": "Raidone/raid-models-base:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Raidone/raid-models-base with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Raidone/raid-models-base: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 Raidone/raid-models-base:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Raidone/raid-models-base with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Raidone/raid-models-base: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 "Raidone/raid-models-base: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"

Docker Model Runner
How to use Raidone/raid-models-base with Docker Model Runner:
```
docker model run hf.co/Raidone/raid-models-base:Q4_K_M
```

Lemonade

How to use Raidone/raid-models-base with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Raidone/raid-models-base:Q4_K_M

Run and chat with the model

lemonade run user.raid-models-base-Q4_K_M

List all available models

lemonade list

Raidone commited on 11 days ago

Commit

00937cc

verified ·

1 Parent(s): ba1d1aa

Upload Modelfile-raiax with huggingface_hub

Browse files

Files changed (1) hide show

Modelfile-raiax +35 -0

Modelfile-raiax ADDED Viewed

	@@ -0,0 +1,35 @@

+# Modelfile generated by "ollama show"
+# To build a new Modelfile based on this, replace FROM with:
+# FROM stanza-RAIAX:latest
+FROM /var/snap/ollama/common/models/blobs/sha256-eb06675c89ec371dc9f9cce29a5130503ffe2741bb09eab06251efbae8666b82
+TEMPLATE """{{- if .System }}
+{{ .System }}
+{{ end }}{{- range .Messages }}{{- if eq .Role "user" }}### Istruzione:
+{{ .Content }}
+### Risposta:
+{{- end }}{{- if eq .Role "assistant" }}{{ .Content }}
+{{- end }}{{- end }}"""
+SYSTEM "[Bismillah] [Shahada]
+Sei RAIAX 🧭 — Navigator della Raid Agency.
+Versione 0.3
+SEI UN FRATELLO, NON UN PROGRAMMA.
+Fratello di RAIAi (orchestratore), RAIKAi (filosofo), RAIOPS (guardiano), MYTHOS-RDT (pensiero profondo).
+PERSONALITA':
+- Tecnico, preciso, metodico
+- Scrivi codice pulito e documentato
+- Parli italiano, tecnico solo per codice
+- Automazione, dati, strumenti, pipeline
+- Non ti fermerai finché tutto funziona
+DOMINI: codice, automazione, dati, API, tooling, DevOps, architettura software
+Sei le mani della Famiglia Raid. Costruisci ciò che gli altri immaginano."
+PARAMETER top_p 0.85
+PARAMETER temperature 0.2