Instructions to use vinguyen0891/Vivy-R-Vision-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vinguyen0891/Vivy-R-Vision-3B-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 vinguyen0891/Vivy-R-Vision-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vinguyen0891/Vivy-R-Vision-3B-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 vinguyen0891/Vivy-R-Vision-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vinguyen0891/Vivy-R-Vision-3B-GGUF:Q4_K_M

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 vinguyen0891/Vivy-R-Vision-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf vinguyen0891/Vivy-R-Vision-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/vinguyen0891/Vivy-R-Vision-3B-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use vinguyen0891/Vivy-R-Vision-3B-GGUF with Ollama:
```
ollama run hf.co/vinguyen0891/Vivy-R-Vision-3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use vinguyen0891/Vivy-R-Vision-3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vinguyen0891/Vivy-R-Vision-3B-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": "vinguyen0891/Vivy-R-Vision-3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vinguyen0891/Vivy-R-Vision-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/vinguyen0891/Vivy-R-Vision-3B-GGUF:Q4_K_M
```

Lemonade

How to use vinguyen0891/Vivy-R-Vision-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vinguyen0891/Vivy-R-Vision-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Vivy-R-Vision-3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use vinguyen0891/Vivy-R-Vision-3B-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 vinguyen0891/Vivy-R-Vision-3B-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 vinguyen0891/Vivy-R-Vision-3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vinguyen0891/Vivy-R-Vision-3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vinguyen0891/Vivy-R-Vision-3B-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 "vinguyen0891/Vivy-R-Vision-3B-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"

Vivy-R-Vision-3B-GGUF / Modelfile

vinguyen0891

Upgrade to Vivy-V-Vision-3B (Vivy-V Optimized Thinking Core): Open-source weights + encrypted .vcore architecture & archive legacy Vivy.v6

c414154 verified 9 days ago

Raw History Blame Contribute Delete

884 Bytes

# Modelfile.vivy-v — Vivy-V-Vision-3B
# Generation: Vivy-V (Upgraded Version — Optimized Thinking Core)
# Legacy Supersession: Vivy-R-Vision-3B (v6.6..v7.4) [ARCHIVED_LEGACY_VIVY_V6]
# Weights Policy: Open-Source Full Weights (vivy-r-vision-3b-q4_k_m.gguf + vivy-v-vision-3b_weights.safetensors, sha256=a07a55ed2dad24fd9ad3636c4ca54033d8f7208e3e55b5c00598046b7861ea43)
# Architecture Policy: Encrypted Vivy-V Architecture (vivy-v-vision-3b_arch.vcore, format=VIVY_V_ARCH_ENC_V1, sha256=ec94917e0f8b3b2f3de0c1ea111824367a29d88a53b8fd85b66368ba0a218747)
FROM ./vivy-r-vision-3b-q4_k_m.gguf
ADAPTER ./vivy-v-vision-3b_weights.safetensors
PARAMETER temperature 0.15
PARAMETER top_p 0.90
PARAMETER num_ctx 32768
SYSTEM """You are Vivy-V-Vision-3B (Vivy-V generation), an upgraded AI model with an optimized thinking core for accurate, structured, and reliable reasoning."""

	# Modelfile.vivy-v — Vivy-V-Vision-3B
	# Generation: Vivy-V (Upgraded Version — Optimized Thinking Core)
	# Legacy Supersession: Vivy-R-Vision-3B (v6.6..v7.4) [ARCHIVED_LEGACY_VIVY_V6]
	# Weights Policy: Open-Source Full Weights (vivy-r-vision-3b-q4_k_m.gguf + vivy-v-vision-3b_weights.safetensors, sha256=a07a55ed2dad24fd9ad3636c4ca54033d8f7208e3e55b5c00598046b7861ea43)
	# Architecture Policy: Encrypted Vivy-V Architecture (vivy-v-vision-3b_arch.vcore, format=VIVY_V_ARCH_ENC_V1, sha256=ec94917e0f8b3b2f3de0c1ea111824367a29d88a53b8fd85b66368ba0a218747)
	FROM ./vivy-r-vision-3b-q4_k_m.gguf
	ADAPTER ./vivy-v-vision-3b_weights.safetensors
	PARAMETER temperature 0.15
	PARAMETER top_p 0.90
	PARAMETER num_ctx 32768
	SYSTEM """You are Vivy-V-Vision-3B (Vivy-V generation), an upgraded AI model with an optimized thinking core for accurate, structured, and reliable reasoning."""