---
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.