Instructions to use JANGQ-AI/GLM-5.3-Flash-JANGH2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JANGQ-AI/GLM-5.3-Flash-JANGH2 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("JANGQ-AI/GLM-5.3-Flash-JANGH2")
config = load_config("JANGQ-AI/GLM-5.3-Flash-JANGH2")

# 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 JANGQ-AI/GLM-5.3-Flash-JANGH2 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "JANGQ-AI/GLM-5.3-Flash-JANGH2"

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": "JANGQ-AI/GLM-5.3-Flash-JANGH2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use JANGQ-AI/GLM-5.3-Flash-JANGH2 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 "JANGQ-AI/GLM-5.3-Flash-JANGH2"

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 JANGQ-AI/GLM-5.3-Flash-JANGH2

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use JANGQ-AI/GLM-5.3-Flash-JANGH2 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "JANGQ-AI/GLM-5.3-Flash-JANGH2"

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 "JANGQ-AI/GLM-5.3-Flash-JANGH2" \
  --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"

jangq commited on 9 days ago

Commit

4017296

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1 Parent(s): b2c5e34

Runtime note: vMLX only

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@@ -31,7 +31,7 @@ tags:
 > ✅ **Runtime: supported in vMLX (Python).** Use **vMLX 1.6.68 or newer** (native JANGH loading; the vMLX 1.6.71
 > release was validated with GLM JANGH on native reasoning efforts and chat tool/media continuations). Older vMLX builds refuse the
-> bundle at load time rather than producing wrong output. Osaurus **0.25.15 or newer** runs it as well (`OsaurusAI/GLM-5.3-Flash-JANGH2`).
 > Known limitation (vMLX 1.6.71 notes): video perception is imperfect — the model can describe overlay or
 > transition text that is not in the clip.

 > ✅ **Runtime: supported in vMLX (Python).** Use **vMLX 1.6.68 or newer** (native JANGH loading; the vMLX 1.6.71
 > release was validated with GLM JANGH on native reasoning efforts and chat tool/media continuations). Older vMLX builds refuse the
+> bundle at load time rather than producing wrong output.
 > Known limitation (vMLX 1.6.71 notes): video perception is imperfect — the model can describe overlay or
 > transition text that is not in the clip.