Model reference · open weights
LFM2.5-ONNX is an open-weight language model from LiquidAI.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the LiquidAI/LFM2.5-230M-ONNX model card, 2026-10-01
What it is
| Released by | LiquidAI |
|---|---|
| Released | 2026-06-15 |
From the model card
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;" />
ONNX export of LFM2.5-230M for cross-platform inference.
LFM2.5 is a hybrid architecture combining multiplicative gates and short convolutions, optimized for edge deployment with fast inference on CPU, GPU, and NPU hardware.
| Precision | Size | Platform | Use Case |
|---|---|---|---|
| Q4 | ~200 MB | WebGPU, Server | Recommended for most uses (quantized embedding) |
| Q4F32 | ~390 MB | Server (CPU/GPU) | Q4 weights with FP32 embedding — higher quality |
| FP16 | ~455 MB | WebGPU, Server | Higher quality |
| Q8 | ~470 MB | Server only | Balance of quality and size |
Q4 or FP16 (Q4F32 and Q8 are not supported on WebGPU).Q4F32 keeps the embedding in FP32 for higher fidelity.The tied embedding / LM head is kept in FP32 across all quantized builds.
onnx/
├── model.onnx # FP32
├── model_fp16.onnx # FP16
├── model_q4.onnx # Q4, quantized embedding (WebGPU)
├── model_q4f32.onnx # Q4 weights, FP32 embedding (server)
└── model_q8.onnx # Q8
pip install onnxruntime transformers numpy huggingface_hub
# or, for GPU:
pip install onnxruntime-gpu transformers numpy huggingface_hub
from huggingface_hub import hf_hub_download
model_id = "LiquidAI/LFM2.5-230M-ONNX"
# Q4F32 recommended for server CPU/GPU; use model_q4.onnx for WebGPU.
hf_hub_download(model_id, "onnx/model_q4f32.onnx")
hf_hub_download(model_id, "onnx/model_q4f32.onnx_data")
import { pipeline } from "@huggingface/transformers";
const generator = await pipeline("text-generation", "LiquidAI/LFM2.5-230M-ONNX", {
device: "webgpu",
dtype: "q4", // or "fp16"
});
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.