Instructions to use JANGQ-AI/Ornith-1.0-35B-JANG_6M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JANGQ-AI/Ornith-1.0-35B-JANG_6M 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/Ornith-1.0-35B-JANG_6M")
config = load_config("JANGQ-AI/Ornith-1.0-35B-JANG_6M")

# 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/Ornith-1.0-35B-JANG_6M 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/Ornith-1.0-35B-JANG_6M"

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": "JANGQ-AI/Ornith-1.0-35B-JANG_6M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use JANGQ-AI/Ornith-1.0-35B-JANG_6M 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/Ornith-1.0-35B-JANG_6M"

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/Ornith-1.0-35B-JANG_6M

Run Hermes

hermes

OpenClaw new

How to use JANGQ-AI/Ornith-1.0-35B-JANG_6M 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/Ornith-1.0-35B-JANG_6M"

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/Ornith-1.0-35B-JANG_6M" \
  --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"

Ornith-1.0-35B · JANG_6M

Vision-language · Qwen3.5 hybrid backbone · 6-bit near-lossless mixed precision · ~26 GB

⚠️ Requires MLX Studio (or the vMLX runtime) to run. Standard mlx_lm cannot load JANG bundles correctly — they store the Qwen3.5 RMSNorm un-shifted and rely on the runtime's +1 scale_shift + per-layer bit detection. MLX Studio includes the JANG loader.

JANG_6M = 8-bit attention + 6-bit routed experts (affine mixed precision, group-size 64); vision tower kept fp16. A near-lossless JANG profile from JANGQ-AI.

Architecture


Family	`qwen3_5_moe` (hybrid)
Text layers	40 — 30 Gated-DeltaNet + 10 full-attention
MoE / dims	256 routed experts (stacked switch_mlp) · hidden 2048
Vision	ViT tower (`model.visual`) preserved fp16
Cache	hybrid (GDN state + KV for attention layers)
Parsers	reasoning `qwen3` · tools `qwen`

Provenance

Base: deepreinforce-ai/Ornith-1.0-35B © DeepReinforce — MIT (Qwen3.5-based)
Quantization: JANG · JANG_6M (8-bit attention + 6-bit routed experts, group-size 64; vision tower fp16) · eric@jangq.ai

Downloads last month: 739

Safetensors

Model size

8B params

Tensor type

U32

F16

MLX

Hardware compatibility

Quantized

Model tree for JANGQ-AI/Ornith-1.0-35B-JANG_6M

Base model

deepreinforce-ai/Ornith-1.0-35B

Finetuned

(11)

this model