Instructions to use TiGa-RCE/needle-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TiGa-RCE/needle-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir needle-mlx TiGa-RCE/needle-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| license: mit | |
| library_name: mlx | |
| tags: | |
| - mlx | |
| - function-calling | |
| - tool-use | |
| - encoder-decoder | |
| - apple-silicon | |
| # Needle MLX | |
| An MLX safetensors format conversion of | |
| [Cactus-Compute/needle](https://huggingface.co/Cactus-Compute/needle), a | |
| 26M-parameter encoder-decoder function-calling model. | |
| This is not a fine-tune or a quantization. The published checkpoint is loaded | |
| using Needle's declared `bfloat16` inference dtype, then stored as MLX | |
| safetensors so it preserves the upstream runtime's executable values. | |
| ## What is included | |
| - `model.safetensors`: 31 converted tensors, 26,315,421 parameters. | |
| - `needle_mlx.py`: a small custom MLX inference implementation for Needle's | |
| architecture. | |
| - `tokenizer/`: the upstream SentencePiece tokenizer files. | |
| - `config.json` and `manifest.json`: architecture and conversion provenance. | |
| ## Use | |
| ```python | |
| from needle_mlx import NeedleModel | |
| model = NeedleModel.from_pretrained(".") | |
| logits = model.forward([[1, 2, 3]], [[1, 4]]) | |
| ``` | |
| This is an MLX-native package, not an MLX-LM or oMLX model yet. Needle uses a | |
| custom JAX/Flax architecture, so load it with the included runtime or add a | |
| dedicated adapter to the serving runtime. | |
| ## Conversion and verification | |
| The published `needle.pkl` checkpoint was read with a restricted NumPy-only | |
| pickle loader, converted to MLX `bfloat16` safetensors, and checked | |
| tensor-for-tensor after the same dtype cast used by Needle's upstream runtime. | |
| - Source SHA-256: | |
| `40a32e91d1d4197bf15ba559b74f6727c342dc8746918742fc7d8e2c1f18df40` | |
| - Converted `model.safetensors` SHA-256: | |
| `7b9d5f0d6ddeb7fbb20f4e45f3f616919357e5d08b5778859fdb762a33d60dae` | |
| - Verification: 31/31 tensors equal the source after Needle's `bfloat16` | |
| runtime cast; an MLX encoder-decoder smoke pass produced logits with shape | |
| `(1, 2, 8192)` and generated the upstream weather tool-call example. | |
| The converter source is available at | |
| [seeker-cyber-maker/needle-mlx-depicklinator](https://github.com/seeker-cyber-maker/needle-mlx-depicklinator). | |
| ## Credits and license | |
| Needle was created by Cactus Compute. See the | |
| [upstream model card](https://huggingface.co/Cactus-Compute/needle) and | |
| [source repository](https://github.com/cactus-compute/needle) for the model, | |
| training details, and citation. The upstream model card publishes Needle under | |
| the MIT license; this converted package retains that license. | |