Instructions to use RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32 --local-dir LFM2.5-Encoder-350M-MLX-FP32
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download validation.json from RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32: direct link, hf CLI and curl.
- Browser
- Download file 2.37 kB
-
https://huggingface.co/RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32/resolve/main/validation.json
- Command line
-
hf download hf://RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32/validation.json
-
curl -L -o validation.json https://huggingface.co/RESMP-DEV/LFM2.5-Encoder-350M-MLX-FP32/resolve/main/validation.json
2.37 kB
| { | |
| "source": "LiquidAI/LFM2.5-Encoder-350M", | |
| "source_revision": "b886781f7c6f10ca9b7096e21b83e30a073c2f39", | |
| "runtime": { | |
| "mlx_device": "Device(gpu, 0)", | |
| "torch_device": "cpu", | |
| "dtype": "float32" | |
| }, | |
| "tensor_audit": { | |
| "tensor_count": 148, | |
| "exact_tensor_count": 148, | |
| "parameter_count": 354483968, | |
| "all_source_tensors_fp32": true, | |
| "all_mlx_tensors_fp32": true, | |
| "all_values_bit_exact_after_layout_transform": true, | |
| "source_weights_sha256": "cd70e404c3c6c1756b2cf5dc75de2a87788b460e3a6156c1a4ab134b824c2706", | |
| "mlx_weights_sha256": "59318ef252f74691ff6a5338f1009c9723a91126f6e9f4361f0bdfa41b98c886" | |
| }, | |
| "parity": { | |
| "input_shape": [ | |
| 3, | |
| 13 | |
| ], | |
| "valid_token_count": 31, | |
| "hidden": { | |
| "max_abs_error": 0.0003604888916015625, | |
| "mean_abs_error": 3.0134134043696157e-06, | |
| "rmse": 5.094438406081536e-06, | |
| "relative_l2_error": 4.034416993567579e-06, | |
| "cosine_similarity": 0.9999999999919315 | |
| }, | |
| "logits": { | |
| "max_abs_error": 0.0003662109375, | |
| "mean_abs_error": 1.4175360927475862e-05, | |
| "rmse": 2.2781126167816667e-05, | |
| "relative_l2_error": 2.509927860453007e-06, | |
| "cosine_similarity": 0.9999999999968735 | |
| }, | |
| "top1_agreement_valid_tokens": 1.0, | |
| "top1_agreement_masked_tokens": 1.0, | |
| "masked_predictions": [ | |
| { | |
| "text": "The capital of France is <|mask|>.", | |
| "reference_token_id": 5242, | |
| "mlx_token_id": 5242, | |
| "reference_token": " Paris", | |
| "mlx_token": " Paris", | |
| "match": true | |
| }, | |
| { | |
| "text": "Liquid neural networks run efficiently on <|mask|> devices.", | |
| "reference_token_id": 64014, | |
| "mlx_token_id": 64014, | |
| "reference_token": "python", | |
| "mlx_token": "python", | |
| "match": true | |
| }, | |
| { | |
| "text": "Paris is the <|mask|> of France.", | |
| "reference_token_id": 5706, | |
| "mlx_token_id": 5706, | |
| "reference_token": " capital", | |
| "mlx_token": " capital", | |
| "match": true | |
| } | |
| ] | |
| }, | |
| "acceptance": { | |
| "passed": true, | |
| "checks": { | |
| "bit_exact_fp32_weights": true, | |
| "hidden_relative_l2_below_1e-4": true, | |
| "logits_relative_l2_below_1e-4": true, | |
| "valid_token_top1_agreement_100_percent": true, | |
| "masked_token_top1_agreement_100_percent": true | |
| } | |
| } | |
| } | |