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
| { | |
| "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" | |
| } | |