Instructions to use OsaurusAI/Ornith-1.0-35B-JANG_4M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OsaurusAI/Ornith-1.0-35B-JANG_4M 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("OsaurusAI/Ornith-1.0-35B-JANG_4M")
config = load_config("OsaurusAI/Ornith-1.0-35B-JANG_4M")

# 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 OsaurusAI/Ornith-1.0-35B-JANG_4M with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/Ornith-1.0-35B-JANG_4M"

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": "OsaurusAI/Ornith-1.0-35B-JANG_4M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use OsaurusAI/Ornith-1.0-35B-JANG_4M 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 "OsaurusAI/Ornith-1.0-35B-JANG_4M"

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 OsaurusAI/Ornith-1.0-35B-JANG_4M

Run Hermes

hermes

OpenClaw new

How to use OsaurusAI/Ornith-1.0-35B-JANG_4M with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/Ornith-1.0-35B-JANG_4M"

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 "OsaurusAI/Ornith-1.0-35B-JANG_4M" \
  --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_4M

Official OsaurusAI JANG_4M build of deepreinforce-ai/Ornith-1.0-35B (MIT) — a vision-language model on a Qwen3.5 hybrid backbone. Mixed-precision affine (JANG_4M); runs on Apple Silicon via Osaurus.

~18 GB (from ~70 GB bf16) bundle.
JANG_4M: 8-bit affine attention + 4-bit affine routed experts (mixed precision, group-size 64); embeddings/head per-module; vision tower kept fp16.
Vision-language (image + text → text).

Architecture


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

Running it

JANG bundles use the Qwen3.5 RMSNorm +1 scale_shift applied at runtime, so load them in Osaurus (or the vMLX runtime), which handles it automatically:

osaurus run OsaurusAI/Ornith-1.0-35B-JANG_4M

Note: a plain mlx_lm.generate will not be coherent on a JANG bundle — it omits the +1 norm shift. Use the Osaurus / vMLX runtime (or vmlx_engine.loaders.load_jang), which applies it.

Provenance

Quantization: Osaurus · JANG_4M (8-bit affine attention + 4-bit affine routed experts, group-size 64; vision tower fp16) · eric@osaurus.ai

Downloads last month: 476

Safetensors

Model size

5B params

Tensor type

U32

F16

MLX

Hardware compatibility

Quantized

Model tree for OsaurusAI/Ornith-1.0-35B-JANG_4M

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deepreinforce-ai/Ornith-1.0-35B

Finetuned

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