Instructions to use Shiftedx/ornith-1.0-35b-mxfp8-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shiftedx/ornith-1.0-35b-mxfp8-mlx 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("Shiftedx/ornith-1.0-35b-mxfp8-mlx")
config = load_config("Shiftedx/ornith-1.0-35b-mxfp8-mlx")

# 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 Shiftedx/ornith-1.0-35b-mxfp8-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Shiftedx/ornith-1.0-35b-mxfp8-mlx"

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": "Shiftedx/ornith-1.0-35b-mxfp8-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use Shiftedx/ornith-1.0-35b-mxfp8-mlx 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 "Shiftedx/ornith-1.0-35b-mxfp8-mlx"

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 Shiftedx/ornith-1.0-35b-mxfp8-mlx

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Shiftedx/ornith-1.0-35b-mxfp8-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Shiftedx/ornith-1.0-35b-mxfp8-mlx"

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 "Shiftedx/ornith-1.0-35b-mxfp8-mlx" \
  --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

ornith-1.0-35b-mxfp8-mlx

Vision-preserving MLX conversion of deepreinforce-ai/Ornith-1.0-35B, pinned to revision 5df2ed3f675c7beaa490328cc70bb573b65fb660.

Format

Language body: MXFP8, 8-bit, group size 32
MoE gate overrides: affine 8-bit, group size 64
Vision tower: source BF16, 333 tensors
Architecture: Qwen3.5 MoE multimodal, 40 language layers, 256 experts
Tokenizer, chat template, processor metadata, and vision features preserved from the pinned source

This standard artifact disables MTP metadata and does not include an MTP sidecar.

Local qualification

Structural inspection: pass (1,658 indexed tensors; 512 quantized weights; 80 MoE gate overrides)
Shard index: pass; no stale or missing shards
Strict MLX-LM and MLX-VLM lazy load: pass
Cross-variant tokenizer, template, processor, and vision-shard parity: pass
Vision features: source BF16 tensor payload preserved

Basic use

python -m mlx_vlm.generate \
  --model <local-path-or-repo-id> \
  --image image.jpg \
  --prompt "Describe this image." \
  --max-tokens 256

Review the upstream model card for intended use, training, license, benchmarks, and deployment considerations. Quantization does not change the upstream model's safety characteristics.

Downloads last month: 16

Safetensors

Model size

35B params

Tensor type

U32

BF16

MLX

Hardware compatibility

8-bit

Model tree for Shiftedx/ornith-1.0-35b-mxfp8-mlx

Base model

ornith-ai/Ornith-1.0-35B

Quantized

(170)

this model

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Collection

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