Instructions to use JANGQ-AI/Holo3-35B-A3B-JANGTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JANGQ-AI/Holo3-35B-A3B-JANGTQ 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/Holo3-35B-A3B-JANGTQ")
config = load_config("JANGQ-AI/Holo3-35B-A3B-JANGTQ")

# 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/Holo3-35B-A3B-JANGTQ 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/Holo3-35B-A3B-JANGTQ"

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/Holo3-35B-A3B-JANGTQ"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use JANGQ-AI/Holo3-35B-A3B-JANGTQ 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/Holo3-35B-A3B-JANGTQ"

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/Holo3-35B-A3B-JANGTQ

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use JANGQ-AI/Holo3-35B-A3B-JANGTQ 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/Holo3-35B-A3B-JANGTQ"

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/Holo3-35B-A3B-JANGTQ" \
  --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"

Holo3-35B-A3B-JANGTQ / jang_config.json

jangq

Add Holo3-35B-A3B JANGTQ2 (MLX) + VL tower + README

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	{
	"version": 2,
	"weight_format": "mxtq",
	"profile": "JANGTQ2",
	"source_model": {
	"name": "Holo3-35B-A3B",
	"architecture": "qwen3_5_moe_text"
	},
	"has_vision": true,
	"mxtq_seed": 42,
	"mxtq_bits": {
	"attention": 8,
	"linear_attention": 8,
	"shared_expert": 8,
	"routed_expert": 2,
	"embed_tokens": 8,
	"lm_head": 8
	},
	"quantization": {
	"method": "affine+mxtq",
	"group_size": 64,
	"bits_default": 2
	},
	"capabilities": {
	"reasoning_parser": "qwen3",
	"tool_parser": "qwen",
	"think_in_template": true,
	"supports_tools": true,
	"supports_thinking": true,
	"family": "qwen3_5_moe",
	"modality": "vision",
	"cache_type": "hybrid"
	}
	}