Image-Text-to-Text
MLX
Safetensors
Cosmos
cosmos3_edge
vision-language
video
apple-silicon
conversational
Instructions to use netprojects/Cosmos3-Edge-mlx-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use netprojects/Cosmos3-Edge-mlx-6bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("netprojects/Cosmos3-Edge-mlx-6bit") config = load_config("netprojects/Cosmos3-Edge-mlx-6bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Cosmos
How to use netprojects/Cosmos3-Edge-mlx-6bit with Cosmos:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use netprojects/Cosmos3-Edge-mlx-6bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "netprojects/Cosmos3-Edge-mlx-6bit"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "netprojects/Cosmos3-Edge-mlx-6bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use netprojects/Cosmos3-Edge-mlx-6bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "netprojects/Cosmos3-Edge-mlx-6bit"
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 netprojects/Cosmos3-Edge-mlx-6bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use netprojects/Cosmos3-Edge-mlx-6bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "netprojects/Cosmos3-Edge-mlx-6bit"
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 "netprojects/Cosmos3-Edge-mlx-6bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
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Download PRIVACY.md from netprojects/Cosmos3-Edge-mlx-6bit: direct link, hf CLI and curl.
- Browser
- Download file 1.22 kB
-
https://huggingface.co/netprojects/Cosmos3-Edge-mlx-6bit/resolve/main/PRIVACY.md
- Command line
-
hf download hf://netprojects/Cosmos3-Edge-mlx-6bit/PRIVACY.md
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curl -L -o PRIVACY.md https://huggingface.co/netprojects/Cosmos3-Edge-mlx-6bit/resolve/main/PRIVACY.md
1.22 kB
Privacy
| Privacy Information |
|---|
| The model was trained on large-scale publicly available data that may contain images, audio-video, and text relating to people. NVIDIA collected and used this data in compliance with applicable data protection and privacy laws. This model was not designed to derive insights or otherwise learn from any personal data contained in the datasets. |
| NVIDIA uses a combination of filters, data minimization techniques, and other guardrails to help prevent personal data from being recited by our models. We employ automated tools and data processing techniques during pre-training or training to identify and filter certain categories of personal data. For example, for text-bearing source and document components, our automated tools identified potential personal data such as person names, locations, and possible business or public-facing contact information such as email addresses and phone numbers. We reviewed and removed any verified instances of personal data through a combination of automated filtering and human-in-the-loop validation. |
| Please review NVIDIA's Privacy Policy for more information. |