Instructions to use josefprusa/GLM-5.2-Vision-MLX-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use josefprusa/GLM-5.2-Vision-MLX-adapter 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("josefprusa/GLM-5.2-Vision-MLX-adapter")
config = load_config("josefprusa/GLM-5.2-Vision-MLX-adapter")

# 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 josefprusa/GLM-5.2-Vision-MLX-adapter with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "josefprusa/GLM-5.2-Vision-MLX-adapter"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "josefprusa/GLM-5.2-Vision-MLX-adapter"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use josefprusa/GLM-5.2-Vision-MLX-adapter with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "josefprusa/GLM-5.2-Vision-MLX-adapter"

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 "josefprusa/GLM-5.2-Vision-MLX-adapter" \
  --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"

Hermes Agent

How to use josefprusa/GLM-5.2-Vision-MLX-adapter 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 "josefprusa/GLM-5.2-Vision-MLX-adapter"

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 josefprusa/GLM-5.2-Vision-MLX-adapter

Run Hermes

hermes

GLM-5.2-Vision-MLX-adapter

File size: 685 Bytes

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{
 "auto_map": {
  "AutoProcessor": "kimi_k25_processor.KimiK25Processor",
  "AutoImageProcessor": "kimi_k25_vision_processing.KimiK25VisionProcessor"
 },
 "media_proc_cfg": {
  "in_patch_limit": 16384,
  "patch_size": 14,
  "image_mean": [
   0.5,
   0.5,
   0.5
  ],
  "image_std": [
   0.5,
   0.5,
   0.5
  ],
  "merge_kernel_size": 2,
  "fixed_output_tokens": null,
  "patch_limit_on_one_side": 512,
  "in_patch_limit_each_frame": 16384,
  "in_patch_limit_video": null,
  "sample_fps": 2.0,
  "max_num_frames_each_video": null,
  "temporal_merge_kernel_size": 4,
  "timestamp_mode": "hh:mm:ss.fff",
  "config_type": "media_proc.processors.moonvit.MoonViTMediaProcessorConfig"
 }
}