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
File size: 717 Bytes
980b1b3 64bce88 980b1b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"vocab_size": 8192,
"d_model": 512,
"num_heads": 8,
"num_kv_heads": 4,
"num_encoder_layers": 12,
"num_decoder_layers": 8,
"d_ff": 2048,
"max_seq_len": 1024,
"pad_token_id": 0,
"rope_theta": 10000.0,
"dtype": "bfloat16",
"activation": "swiglu",
"num_memory_slots": 64,
"n_mels": 80,
"dropout_rate": 0.1,
"contrastive_dim": 128,
"enable_speech": false,
"no_feedforward": true,
"model_type": "needle_mlx",
"architectures": [
"NeedleModel"
],
"format": "mlx-safetensors",
"format_version": 2,
"tensor_dtype": "bfloat16",
"eos_token_id": 1,
"source_checkpoint": "needle.pkl",
"source_sha256": "40a32e91d1d4197bf15ba559b74f6727c342dc8746918742fc7d8e2c1f18df40"
}
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