The published 8da4w files predate the quantization sweep: they carry no quantized embedding table, which is why they are ~176 MB larger than these.
- 350m: group_size 32, embedding_quantize 8,32
- 1_2b: group_size 32, embedding_quantize 8,0
Also fixes the export emitting bf16 logits: the published artifacts and every config declare float32, and the quantized branch was overriding the dtype to bf16, so any re-export silently changed the output contract.
Configs gain get_n_layers (16), which was null in all four. The fp16 and MLX variants are untouched. Both .pte were probed against their config and match.
Update chat template for strict monotonicity and tool support
---
license: other
license_name: lfm1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- executorch
- liquid
- lfm2.5
- edge
---
# Introduction
This repository hosts the **LFM2.5** model family for the [React Native ExecuTorch](https://www.npmjs.com/package/react-native-executorch) library. It includes exported models in `.pte` format, ready for use in the **ExecuTorch** runtime.
If you'd like to run these models in your own ExecuTorch runtime, refer to the [official documentation](https://pytorch.org/executorch/stable/index.html) for setup instructions.
## Compatibility
If you intend to use this model outside of React Native ExecuTorch, make sure your runtime is compatible with the **ExecuTorch** version used to export the `.pte` files. For more details, see the compatibility note in the [ExecuTorch GitHub repository](https://github.com/pytorch/executorch/blob/11d1742fdeddcf05bc30a6cfac321d2a2e3b6768/runtime…
