Instructions to use ToPo-ToPo/Ornith-1.5-122B-MTP-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ToPo-ToPo/Ornith-1.5-122B-MTP-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download ToPo-ToPo/Ornith-1.5-122B-MTP-bf16 --local-dir Ornith-1.5-122B-MTP-bf16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
ToPo-ToPo/Ornith-1.5-122B-MTP-bf16
MTP (multi-token prediction) drafter for speculative decoding with
ornith-ai/Ornith-1.5-122B on Apple Silicon (mlx-vlm).
This is not a standalone chat model — it only works bound to an Ornith-1.5-122B target model.
The official MLX conversions (MLX,
MLX-8bit,
MLX-4bit) do not include the MTP weights.
Provenance
- Split from the official checkpoint's built-in
mtp.*weights (20 tensors) with mlx-vlm 0.7.6 (mlx_vlm.speculative.drafters.qwen3_5_mtp.split, mlx 0.32.3) - Precision: bf16 (unquantized, split as-is)
- Size: 5.0 GB,
block_size: 3,model_type: qwen3_5_mtp
Usage
mlx_vlm.generate --model ornith-ai/Ornith-1.5-122B-MLX-8bit \
--draft-model ToPo-ToPo/Ornith-1.5-122B-MTP-bf16 --draft-kind mtp \
--prompt "..." --max-tokens 400
Tested bound to an 8bit (affine, group size 64) conversion of Ornith-1.5-122B, served through mlx-vlm 0.7.9. Speedup depends on hardware, target quantization, workload and the mlx-vlm version — measure it yourself.
- Downloads last month
- -
Model size
3B params
Tensor type
BF16
·
Hardware compatibility
Log In to add your hardware
Quantized
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for ToPo-ToPo/Ornith-1.5-122B-MTP-bf16
Base model
ornith-ai/Ornith-1.5-122B