Instructions to use prithivMLmods/Video-ORA-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Video-ORA-4B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Video-ORA-4B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Video-ORA-4B-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 prithivMLmods/Video-ORA-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Video-ORA-4B-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 prithivMLmods/Video-ORA-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Video-ORA-4B-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 prithivMLmods/Video-ORA-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Video-ORA-4B-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 prithivMLmods/Video-ORA-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Video-ORA-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Video-ORA-4B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use prithivMLmods/Video-ORA-4B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Video-ORA-4B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/Video-ORA-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Video-ORA-4B-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": "prithivMLmods/Video-ORA-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/Video-ORA-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Video-ORA-4B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Video-ORA-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Video-ORA-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Video-ORA-4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/Video-ORA-4B-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 prithivMLmods/Video-ORA-4B-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 prithivMLmods/Video-ORA-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/Video-ORA-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Video-ORA-4B-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 "prithivMLmods/Video-ORA-4B-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-ORA-4B-GGUF

Video-ORA-4B is a 4-billion-parameter unified video understanding model built on Qwen3.5-4B and post-trained with OraRL (Annotations as Rollouts) — an annotation-augmented, on-policy reinforcement learning method that equips a single compact model to handle seven task families with direct, task-native answers and no chain-of-thought decoding: temporal grounding, visual tracking, image/video segmentation, spatial grounding, spatial-temporal grounding, video question answering, and spatial intelligence. It retains a 262,144-token native context window from its base model and delivers strong compact-model results in matched seven-family benchmark comparisons against multimodal baselines despite skipping reasoning traces at inference, with evaluations run using direct-answer prompts (enable_thinking=False) to match its reported protocol. The model is served via vLLM (with a qwen3 reasoning parser and configurable video frame sampling) or a Transformers serving endpoint, occupies roughly 8.6 GiB in BF16 weight loading, and is intended for research on structured video/spatial perception, benchmark evaluation, and task-specific adaptation — explicitly out of scope for safety-critical decisions, identity inference, or surveillance deployment. Trained on public dataset splits with evaluation identities and media excluded from the training mixture, it is released under the Apache License 2.0, consistent with its Qwen3.5-4B base, and serves as the smaller, more deployment-friendly sibling to Video-ORA-9B in the OraRL model family.

Model: https://huggingface.co/OraRL/Video-ORA-4B

Model Files

File Name	Quant Type	File Size	File Link
Video-ORA-4B.BF16.gguf	BF16	9.7 GB	Download
Video-ORA-4B.F16.gguf	F16	9.7 GB	Download
Video-ORA-4B.Q3_K_L.gguf	Q3_K_L	2.69 GB	Download
Video-ORA-4B.Q3_K_M.gguf	Q3_K_M	2.54 GB	Download
Video-ORA-4B.Q3_K_S.gguf	Q3_K_S	2.34 GB	Download
Video-ORA-4B.Q4_0.gguf	Q4_0	2.9 GB	Download
Video-ORA-4B.Q4_K_M.gguf	Q4_K_M	3.07 GB	Download
Video-ORA-4B.Q4_K_S.gguf	Q4_K_S	2.92 GB	Download
Video-ORA-4B.Q5_0.gguf	Q5_0	3.43 GB	Download
Video-ORA-4B.Q5_K_M.gguf	Q5_K_M	3.51 GB	Download
Video-ORA-4B.Q5_K_S.gguf	Q5_K_S	3.43 GB	Download
Video-ORA-4B.mmproj-bf16.gguf	mmproj-bf16	676 MB	Download
Video-ORA-4B.mmproj-f16.gguf	mmproj-f16	676 MB	Download