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
- MLX
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
- Pi
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 @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": "Shiftedx/ornith-1.0-35b-mxfp8-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
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
Ornith-1.0-35B MLX MXFP8 Vision
This is an unofficial community MLX MXFP8 quantization of
deepreinforce-ai/Ornith-1.0-35B,
prepared by shiftedx for Apple Silicon and LM Studio.
The build is vision-enabled. It combines the quantized language model with an
MLX-compatible vision_tower shard and processor files.
Build
- Base model:
deepreinforce-ai/Ornith-1.0-35B - Format: MLX safetensors
- Quantization: MXFP8, 8-bit, group size 32
- MoE router/gate layers: 8-bit affine
- Indexed tensor bytes: 36,638,678,752
- Indexed parameters: 35,107,181,936
- Shards: 28 safetensors files
- Vision tensors: 333
- Context metadata: 262,144 max context
Compatibility
Validated locally in LM Studio on Apple Silicon:
- Load key:
ornith-1.0-35b-mxfp8-mlx - Runtime context used for validation: 32,768
- Resident memory at 32k context: about 34.15 GiB
- Text smoke: passed
- Vision smoke: passed
The vision config includes a small compatibility adjustment for current
MLX/LM Studio loaders: vision_config.model_type is set to qwen3_5_moe.
Lightweight Validation
This is not an official HumanEval leaderboard result. It is a deterministic local smoke test intended to catch obvious quantization or packaging regressions.
| Test | Result |
|---|---|
HumanEval test[:20] via LM Studio /v1/completions |
18/20 |
| Pass rate | 90% |
| Temperature | 0 |
| Max tokens | 512 |
| Harness note | First-line indentation normalization enabled |
Failures in the local smoke run:
HumanEval/8HumanEval/19
Suggested Generation Settings
For general use:
- Temperature: 0.6
- Top-p: 0.95
- Top-k: 20
- Min-p: 0
For deterministic code evaluation, use temperature 0.
Attribution
This quantization is derived from
deepreinforce-ai/Ornith-1.0-35B.
The upstream model card declares the model MIT licensed. This repository is not
an official Deep Reinforce release.
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Base model
deepreinforce-ai/Ornith-1.0-35BEvaluation results
- pass@1 on openai/openai_humaneval test[:20]self-reported90.000