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

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

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"

GLM-5.3-Flash-4bit-paged / README.md

yanghaojin

Describe image input

9434359 verified 1 day ago

preview code

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metadata

language:
  - en
  - zh
license: mit
base_model: pipenetwork/GLM-5.3-Flash-MLX-4bit
library_name: mlx
pipeline_tag: image-text-to-text
tags:
  - mlx
  - gbx-lm
  - apple-silicon
  - expert-paging
  - mixture-of-experts
  - multimodal

GLM-5.3-Flash-4bit-paged

Expert-paged build of pipenetwork/GLM-5.3-Flash-MLX-4bit. The weights that are read a fraction at a time live in their own containers, so a machine loads what it needs rather than all of it.

file	size	holds
`model.safetensors`	5.89 GiB	resident weights
`experts.bin`	159.47 GiB	routed experts
`mtp/`	3.90 GiB	draft head, off by default

Total 169.27 GiB. Of that, 165.37 GiB is the source build, whose bytes moved into

These containers are not a format mlx-lm reads. The model runs on gbx_lm, a single signed binary for Apple Silicon; there is nothing to pip install. containers rather than being copied, and 3.90 GiB is the draft head, which no published build of this model carries.

Requirements

Mac


macOS	15.0 or later
chip	Apple Silicon (arm64). There is no Intel build.
Python	none -- the binary carries what it needs

Linux (gbx-lm v0.8.1 or later)


machine	NVIDIA DGX Spark (GB10). Other Linux machines are not supported yet.
system	arm64, glibc 2.39 or later (DGX OS / Ubuntu 24.04)
driver	an NVIDIA driver with CUDA 13 support. No CUDA toolkit is needed: the binary carries the CUDA libraries it uses.
Python	none

On a DGX Spark (128 GB) the model does not fit, so its experts are paged from the SSD.

Memory is not a fixed figure for a paged build, and that is the point of one: it fills what fits and streams the rest from disk. On a 512 GB Mac Studio with room to spare this model settles at about 172 GB resident. A smaller machine holds less and reads more from disk -- slower, but it runs.

How much slower depends on how far the machine is from holding the experts, and on how fast its disk is. Each token routes to a few experts; the ones already in memory cost nothing to reach, and the ones that are not have to be read before that token can finish. A machine holding most of them waits rarely, one holding few waits often. We have not measured this across machine sizes and will not guess a figure: what we can say is that the model answers either way, and that the wait is the SSD's, not the model's.

Install

# upgrading? clear the previous version's unpack directory first
rm -rf ~/.libra/cache/onefile/gbx_lm

# on a DGX Spark, use gbx_lm-linux-arm64.tar.gz in place of gbx_lm-darwin-arm64.tar.gz
curl -fL -o gbx_lm-darwin-arm64.tar.gz 'https://github.com/GreenBitAI/gbx-lm/releases/latest/download/gbx_lm-darwin-arm64.tar.gz' \
  && tar -xzf gbx_lm-darwin-arm64.tar.gz gbx_lm \
  && mkdir -p "$HOME/.local/bin" \
  && mv gbx_lm "$HOME/.local/bin/gbx_lm" \
  && chmod +x "$HOME/.local/bin/gbx_lm"

gbx_lm -h

The Mac build is signed with a Developer ID and notarised, so macOS runs it without the usual detour for a downloaded binary.

command not found -- $HOME/.local/bin is not on your PATH:

echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.zshrc && source ~/.zshrc     # zsh
echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bash_profile && source ~/.bash_profile   # bash

Killed: 9 (Mac) -- a previous version's files are still in the unpack directory, and macOS refuses to mix two builds. Run the rm -rf line above, then try again.

Run

gbx_lm --model GreenBitAI/GLM-5.3-Flash-4bit-paged

That serves an OpenAI-compatible API on port 11688, which is its default. The weights download on first use into ~/.libra/cache/models; set HF_HOME to put them elsewhere, and HF_TOKEN if you meet the Hub's rate limits for anonymous downloads.

curl http://127.0.0.1:11688/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"GreenBitAI/GLM-5.3-Flash-4bit-paged","messages":[{"role":"user","content":"Hello"}]}'

Where the weights fit they are filled from experts.bin and the model runs the stock path at stock speed; where they do not, they stream from disk. Reading the machine decides that, not a flag.

To override that: GBX_PAGING=off holds the experts resident.

Checked at build time, while the source checkpoint was still there to compare against:

PASS layer-wise vs resident — 42 layers x 2 draws exact, 60.93 GiB peak for this gate

Quantization, tokenizer, chat template and licence are unchanged from pipenetwork/GLM-5.3-Flash-MLX-4bit.

Images

The model reads images as well as text. Send them as OpenAI image_url parts -- an http(s) URL or a base64 data: URL:

curl http://127.0.0.1:11688/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"GreenBitAI/GLM-5.3-Flash-4bit-paged","messages":[{"role":"user","content":[
        {"type":"text","text":"What is in this picture?"},
        {"type":"image_url","image_url":{"url":"https://example.com/photo.jpg"}}]}]}'

From the command line, --images takes one or more files: gbx_lm generate --model GreenBitAI/GLM-5.3-Flash-4bit-paged --images photo.jpg --prompt "What is in this picture?" --max-tokens 2048.

From the command line

From gbx_lm v0.7.1, the binary also runs the model without a server -- one prompt, or an interactive chat:

GBX_GLM53_MTP=on gbx_lm generate --model GreenBitAI/GLM-5.3-Flash-4bit-paged --prompt "Hello" --max-tokens 2048
GBX_GLM53_MTP=on gbx_lm chat --model GreenBitAI/GLM-5.3-Flash-4bit-paged --max-tokens 2048

gbx_lm generate -h and gbx_lm chat -h list their options. Keep --max-tokens generous: the default is 100 for generate and 256 for chat, and the model's reasoning before it answers counts against it. Leave out GBX_GLM53_MTP=on to run without the draft head.

Coding agents

The server speaks three wire protocols on the same port, so the tools that expect a hosted API can be pointed at this one:

path	for
`/v1/chat/completions`	anything written against the OpenAI API
`/v1/responses`	Codex
`/v1/messages`	Claude Code

Codex -- a provider in ~/.codex/config.toml:

[model_providers.gbx]
name = "gbx-lm"
base_url = "http://127.0.0.1:11688/v1"
wire_api = "responses"

and a profile in ~/.codex/gbx.config.toml:

model_provider = "gbx"
model = "GreenBitAI/GLM-5.3-Flash-4bit-paged"
model_context_window = 1048576

Claude Code -- ~/.claude/gbx.settings.json:

{
  "env": {
    "ANTHROPIC_BASE_URL": "http://127.0.0.1:11688",
    "ANTHROPIC_AUTH_TOKEN": "local",
    "ANTHROPIC_MODEL": "GreenBitAI/GLM-5.3-Flash-4bit-paged",
    "ANTHROPIC_DEFAULT_HAIKU_MODEL": "GreenBitAI/GLM-5.3-Flash-4bit-paged"
  }
}

Both clients ask for a small model for their own background work, so every name in the settings has to be one this server is serving.

The draft head

The mtp/ folder carries the model's own multi-token prediction head, converted from zai-org/GLM-5.3-Flash, so speculative decoding works from this repository alone. It is off unless asked for:

GBX_GLM53_MTP=on gbx_lm --model GreenBitAI/GLM-5.3-Flash-4bit-paged

Up to 1.4x, more as the context grows. Measured 2026-10-13 with gbx_lm v0.8.0 on a 512 GB Mac Studio (M3 Ultra), weights resident, greedy, thinking off, 256 tokens a reply, decode timed from the first token. Short prompts: three each of code, prose and chat, three runs each, the median per kind. Long contexts: a slice of source code and a question about it, the median of three runs.

prompt	head off	head on	speedup
short, code	39.5 tok/s	55.5	1.41x
short, prose	39.5	45.8	1.16x
short, chat	39.5	50.8	1.29x
3,400-token context	32.6	40.0	1.23x
14,000-token context	32.2	43.8	1.36x
29,000-token context	31.4	42.5	1.35x

Code gains most and prose least. With more context behind it the head proposes better -- it accepts about 63% of what it proposes on short prompts and 75-77% from 14k tokens on.

Every token the head proposes is checked by the model itself, so the reply is the model's own either way; the head only saves passes over the weights.