Instructions to use off-grid-ai/Video-Companions-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 off-grid-ai/Video-Companions-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 off-grid-ai/Video-Companions-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf off-grid-ai/Video-Companions-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 off-grid-ai/Video-Companions-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf off-grid-ai/Video-Companions-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 off-grid-ai/Video-Companions-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf off-grid-ai/Video-Companions-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 off-grid-ai/Video-Companions-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf off-grid-ai/Video-Companions-GGUF:Q4_K_M

Use Docker

docker model run hf.co/off-grid-ai/Video-Companions-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use off-grid-ai/Video-Companions-GGUF with Ollama:
```
ollama run hf.co/off-grid-ai/Video-Companions-GGUF:Q4_K_M
```
Unsloth Desktop

How to use off-grid-ai/Video-Companions-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf off-grid-ai/Video-Companions-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": "off-grid-ai/Video-Companions-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use off-grid-ai/Video-Companions-GGUF with Docker Model Runner:
```
docker model run hf.co/off-grid-ai/Video-Companions-GGUF:Q4_K_M
```

Lemonade

How to use off-grid-ai/Video-Companions-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull off-grid-ai/Video-Companions-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Video-Companions-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use off-grid-ai/Video-Companions-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 off-grid-ai/Video-Companions-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 off-grid-ai/Video-Companions-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use off-grid-ai/Video-Companions-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf off-grid-ai/Video-Companions-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 "off-grid-ai/Video-Companions-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"

Video models — companion files

Text encoders and a VAE for the Off Grid video GGUF generators with stable-diffusion.cpp. These files do not generate video by themselves. Use them with one generator from the repositories below.

Files

File	Role	sd-cli flag	Size	Used by
`umt5-xxl-encoder-Q4_K_M.gguf`	UMT5-XXL text encoder	`--t5xxl`	3.655 GB	Wan2.1-T2V-1.3B-GGUF, Wan2.2-TI2V-5B-GGUF
`wan_2.1_vae.safetensors`	Wan 2.1 VAE	`--vae`	0.254 GB	Wan2.1-T2V-1.3B-GGUF, LingBot-Video-Dense-1.3B-GGUF
`Qwen3VL-4B-Instruct-Q4_K_M.gguf`	Qwen3-VL 4B Instruct text encoder	`--llm`	2.497 GB	LingBot-Video-Dense-1.3B-GGUF

Sizes use decimal GB. Wan 2.2 also needs wan2.2_vae.safetensors, which stays in Wan2.2-TI2V-5B-GGUF because no other model uses it.

All files are unchanged copies of the source files. Checksums are in SHA256SUMS.

umt5-xxl-encoder-Q4_K_M.gguf is from city96/umt5-xxl-encoder-gguf at revision b535255bee98c2b0a59ea7c0ae2dcd0c6657b3b7.
wan_2.1_vae.safetensors is from Comfy-Org/Wan_2.1_ComfyUI_repackaged at revision 123acf1cc74bccbb9bfff8ac1ee72edc08c2341d.
Qwen3VL-4B-Instruct-Q4_K_M.gguf is from Qwen/Qwen3-VL-4B-Instruct-GGUF at revision 1cd86afb9a95c410a6038ab3b40d8b578c892266.

The Hugging Face GGUF panel shows one GGUF file per repository. Here it describes one of the two text encoders. Neither file is a video generator.

These files were in the generator repositories before 2026-10-21. Links pinned to an earlier commit of those repositories still work.

Use with stable-diffusion.cpp

Download the files that your generator needs into the same folder as the generator. For example, for Wan 2.1:

hf download off-grid-ai/Video-Companions-GGUF umt5-xxl-encoder-Q4_K_M.gguf wan_2.1_vae.safetensors --local-dir .

The generator repositories have complete commands.

Credit & license

UMT5-XXL: google/umt5-xxl by Google; GGUF conversion by city96.

Wan 2.1 VAE: Wan-AI/Wan2.1-T2V-1.3B by the Wan team (Alibaba), repackaged by Comfy-Org.

Qwen3-VL-4B-Instruct: Qwen/Qwen3-VL-4B-Instruct by the Qwen team. The upstream model card is kept in provenance/.

License: apache-2.0 for all three files (carried over from the sources). See NOTICE for attribution. This repository redistributes the files unchanged; all credit belongs to the original creators.

Downloads last month: 9

GGUF

Model size

4B params

Architecture

qwen3vl

Hardware compatibility

4-bit

Model tree for off-grid-ai/Video-Companions-GGUF

Base model

Qwen/Qwen3-VL-4B-Instruct

Quantized

(114)

this model