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library_name: mlx
license: other
license_name: lfm1.0
license_link: LICENSE
language:
- ar
- zh
- en
- fr
- de
- hi
- id
- it
- ja
- ko
- pl
- pt
- ru
- es
- th
- vi
pipeline_tag: text-generation
tags:
- liquid
- lfm2.5
- edge
- mlx
base_model: LiquidAI/LFM2.5-2.6B
---
<div align="center">
<img
src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
alt="Liquid AI"
style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
/>
<div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> •
<a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> •
<a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> •
<a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
</div>
</div>
# LFM2.5-2.6B-MLX
LFM2.5 is a new family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-2.6B
## Precisions
Each precision is available both as a standalone repo and as a subfolder of this repo.
| Standalone repo | Folder | Precision | Group Size | Size |
|-----------------|--------|-----------|------------|------|
| [`LiquidAI/LFM2.5-2.6B-MLX-bf16`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX-bf16) | [`bf16/`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX/tree/main/bf16) | bf16 | - | 5.02 GB |
| [`LiquidAI/LFM2.5-2.6B-MLX-8bit`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX-8bit) | [`8bit/`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX/tree/main/8bit) | 8-bit | 64 | 2.67 GB |
| [`LiquidAI/LFM2.5-2.6B-MLX-6bit`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX-6bit) | [`6bit/`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX/tree/main/6bit) | 6-bit | 64 | 2.04 GB |
| [`LiquidAI/LFM2.5-2.6B-MLX-5bit`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX-5bit) | [`5bit/`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX/tree/main/5bit) | 5-bit | 64 | 1.76 GB |
| [`LiquidAI/LFM2.5-2.6B-MLX-4bit`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX-4bit) | [`4bit/`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX/tree/main/4bit) | 4-bit | 64 | 1.47 GB |
| [`LiquidAI/LFM2.5-2.6B-MLX-mxfp8`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX-mxfp8) | [`mxfp8/`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX/tree/main/mxfp8) | MXFP8 | 32 | 2.59 GB |
| [`LiquidAI/LFM2.5-2.6B-MLX-mxfp4`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX-mxfp4) | [`mxfp4/`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX/tree/main/mxfp4) | MXFP4 | 32 | 1.46 GB |
| [`LiquidAI/LFM2.5-2.6B-MLX-nvfp4`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX-nvfp4) | [`nvfp4/`](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX/tree/main/nvfp4) | NVFP4 | 16 | 1.53 GB |
## Use with mlx
```bash
pip install mlx-lm
```
The simplest option is to load a standalone repo directly:
```python
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler
model, tokenizer = load("LiquidAI/LFM2.5-2.6B-MLX-4bit")
response = generate(
model,
tokenizer,
prompt="The capital of France is",
max_tokens=100,
sampler=make_sampler(temp=0.7),
verbose=True,
)
```
If you prefer this repo, note that `mlx_lm.load` does not resolve subfolders of a HuggingFace repo directly, so download the precision you want first:
```python
from huggingface_hub import snapshot_download
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler
path = snapshot_download("LiquidAI/LFM2.5-2.6B-MLX", allow_patterns=["4bit/*"])
model, tokenizer = load(f"{path}/4bit")
response = generate(
model,
tokenizer,
prompt="The capital of France is",
max_tokens=100,
sampler=make_sampler(temp=0.7),
verbose=True,
)
```
|