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license: other
license_name: lfm1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M/blob/main/LICENSE
base_model: LiquidAI/LFM2.5-Encoder-350M
library_name: mlx
tags:
- mlx
- encoder
- lfm2
- liquidai
- fp32
---
# LFM2.5 Encoder 350M — MLX FP32
Native MLX conversion of `LiquidAI/LFM2.5-Encoder-350M`, pinned to revision
`b886781f7c6f10ca9b7096e21b83e30a073c2f39`.
The checkpoint is kept entirely in float32. This implementation preserves the
encoder's full bidirectional attention and centered, non-causal short
convolutions; it is not a causal `mlx-lm` generation model.
```python
import mlx.core as mx
from model import load_model
model = load_model(".")
input_ids = mx.array([[1, 123, 456, 7]])
attention_mask = mx.ones_like(input_ids)
logits = model(input_ids, attention_mask)
mx.eval(logits)
```
Use the included tokenizer files with `transformers.AutoTokenizer` or the
`tokenizers` package. See `validation.json` for the FP32 tensor audit and
PyTorch-to-MLX numerical parity measurements.
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