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"
Update draft-head speed tables (v0.8.0)
Browse files
README.md
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@@ -181,19 +181,24 @@ alone. It is off unless asked for:
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GBX_GLM53_MTP=on gbx_lm --model GreenBitAI/GLM-5.3-Flash-4bit-paged
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```
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GBX_GLM53_MTP=on gbx_lm --model GreenBitAI/GLM-5.3-Flash-4bit-paged
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```
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**Up to 1.4x, more as the context grows.**
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Measured 2026-10-13 with `gbx_lm` v0.8.0 on a 512 GB Mac Studio (M3 Ultra), weights
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resident, greedy, thinking off, 256 tokens a reply, decode timed from the first token.
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Short prompts: three each of code, prose and chat, three runs each, the median per kind.
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Long contexts: a slice of source code and a question about it, the median of three runs.
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| prompt | head off | head on | speedup |
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| short, code | 39.5 tok/s | **55.5** | 1.41x |
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| short, prose | 39.5 | **45.8** | 1.16x |
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| short, chat | 39.5 | **50.8** | 1.29x |
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| 3,400-token context | 32.6 | **40.0** | 1.23x |
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| 14,000-token context | 32.2 | **43.8** | 1.36x |
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| 29,000-token context | 31.4 | **42.5** | 1.35x |
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Code gains most and prose least. With more context behind it the head proposes
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better -- it accepts about 63% of what it proposes on short prompts and 75-77%
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from 14k tokens on.
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Every token the head proposes is checked by the model itself, so the reply is the
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model's own either way; the head only saves passes over the weights.
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