Image-Text-to-Text
MLX
Safetensors
English
Chinese
glm5_next
gbx-lm
apple-silicon
expert-paging
mixture-of-experts
multimodal
conversational
4-bit precision
Instructions to use GreenBitAI/GLM-5.3-Flash-4bit-paged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use GreenBitAI/GLM-5.3-Flash-4bit-paged 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("GreenBitAI/GLM-5.3-Flash-4bit-paged") config = load_config("GreenBitAI/GLM-5.3-Flash-4bit-paged") # 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) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use GreenBitAI/GLM-5.3-Flash-4bit-paged with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "GreenBitAI/GLM-5.3-Flash-4bit-paged"
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": "GreenBitAI/GLM-5.3-Flash-4bit-paged" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use GreenBitAI/GLM-5.3-Flash-4bit-paged 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 "GreenBitAI/GLM-5.3-Flash-4bit-paged"
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 GreenBitAI/GLM-5.3-Flash-4bit-paged
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use GreenBitAI/GLM-5.3-Flash-4bit-paged with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "GreenBitAI/GLM-5.3-Flash-4bit-paged"
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 "GreenBitAI/GLM-5.3-Flash-4bit-paged" \ --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"
Model card: say what a smaller machine trades for fitting
Browse filesPaging already said a machine that cannot hold the experts streams them. This says what that costs -- a wait on the SSD per token that routes to an expert not in memory -- without inventing a number we have not measured.
README.md
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to spare this model settles at about **172 GB** resident. A smaller machine holds
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less and reads more from disk -- slower, but it runs.
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## Install
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```bash
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to spare this model settles at about **172 GB** resident. A smaller machine holds
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less and reads more from disk -- slower, but it runs.
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How much slower depends on how far the machine is from holding the experts, and
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on how fast its disk is. Each token routes to a few experts; the ones already in
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memory cost nothing to reach, and the ones that are not have to be read before
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that token can finish. A machine holding most of them waits rarely, one holding
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few waits often. We have not measured this across machine sizes and will not
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guess a figure: what we can say is that the model answers either way, and that
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the wait is the SSD's, not the model's.
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## Install
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```bash
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