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
Preserve Needle bfloat16 runtime behavior
Browse files- README.md +10 -6
- config.json +2 -1
- manifest.json +2 -1
- model.safetensors +2 -2
README.md
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---
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license: mit
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library_name: mlx
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base_model: Cactus-Compute/needle
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tags:
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- mlx
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- function-calling
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# Needle MLX
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An MLX safetensors conversion of
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[Cactus-Compute/needle](https://huggingface.co/Cactus-Compute/needle), a
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26M-parameter encoder-decoder function-calling model.
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## What is included
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- `model.safetensors`: 31 converted tensors, 26,315,421 parameters.
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## Conversion and verification
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The published `needle.pkl` checkpoint was read with a restricted NumPy-only
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pickle loader, converted
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tensor-for-tensor
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- Source SHA-256:
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`40a32e91d1d4197bf15ba559b74f6727c342dc8746918742fc7d8e2c1f18df40`
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- Converted `model.safetensors` SHA-256:
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`7b9d5f0d6ddeb7fbb20f4e45f3f616919357e5d08b5778859fdb762a33d60dae`
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- Verification: 31/31 tensors equal the source
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pass produced logits with shape
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The converter source is available at
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[seeker-cyber-maker/needle-mlx-depicklinator](https://github.com/seeker-cyber-maker/needle-mlx-depicklinator).
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---
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license: mit
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library_name: mlx
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tags:
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- mlx
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- function-calling
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# Needle MLX
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An MLX safetensors format conversion of
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[Cactus-Compute/needle](https://huggingface.co/Cactus-Compute/needle), a
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26M-parameter encoder-decoder function-calling model.
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This is not a fine-tune or a quantization. The published checkpoint is loaded
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using Needle's declared `bfloat16` inference dtype, then stored as MLX
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safetensors so it preserves the upstream runtime's executable values.
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## What is included
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- `model.safetensors`: 31 converted tensors, 26,315,421 parameters.
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## Conversion and verification
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The published `needle.pkl` checkpoint was read with a restricted NumPy-only
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pickle loader, converted to MLX `bfloat16` safetensors, and checked
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tensor-for-tensor after the same dtype cast used by Needle's upstream runtime.
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- Source SHA-256:
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`40a32e91d1d4197bf15ba559b74f6727c342dc8746918742fc7d8e2c1f18df40`
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- Converted `model.safetensors` SHA-256:
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`7b9d5f0d6ddeb7fbb20f4e45f3f616919357e5d08b5778859fdb762a33d60dae`
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- Verification: 31/31 tensors equal the source after Needle's `bfloat16`
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runtime cast; an MLX encoder-decoder smoke pass produced logits with shape
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`(1, 2, 8192)` and generated the upstream weather tool-call example.
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The converter source is available at
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[seeker-cyber-maker/needle-mlx-depicklinator](https://github.com/seeker-cyber-maker/needle-mlx-depicklinator).
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config.json
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"NeedleModel"
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],
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"format": "mlx-safetensors",
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"format_version":
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"eos_token_id": 1,
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"source_checkpoint": "needle.pkl",
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"source_sha256": "40a32e91d1d4197bf15ba559b74f6727c342dc8746918742fc7d8e2c1f18df40"
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"NeedleModel"
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],
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"format": "mlx-safetensors",
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"format_version": 2,
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"tensor_dtype": "bfloat16",
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"eos_token_id": 1,
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"source_checkpoint": "needle.pkl",
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"source_sha256": "40a32e91d1d4197bf15ba559b74f6727c342dc8746918742fc7d8e2c1f18df40"
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manifest.json
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{
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"format": "needle-mlx",
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"format_version":
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"tensor_count": 31,
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"parameter_count": 26315421,
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"source_sha256": "40a32e91d1d4197bf15ba559b74f6727c342dc8746918742fc7d8e2c1f18df40",
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"source_checkpoint": "needle.pkl"
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}
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{
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"format": "needle-mlx",
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"format_version": 2,
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"tensor_count": 31,
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"parameter_count": 26315421,
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"tensor_dtype": "bfloat16",
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"source_sha256": "40a32e91d1d4197bf15ba559b74f6727c342dc8746918742fc7d8e2c1f18df40",
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"source_checkpoint": "needle.pkl"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:8491dba478deb60e46e437714911e1db729a6463f6289505f99af472b6aac7bc
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size 52634473
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