LFM2-2.6B-Longevity-ONNX

LFM2-2.6B-Longevity - ONNX 4-bit

An ONNX conversion of LiquidAI/LFM2-2.6B-Longevity for ONNX Runtime, quantized to a 4-bit body with 8-bit sensitive layers, for on-device use on Android, desktop and the browser (Transformers.js). The model is Liquid AI and Insilico Medicine's Longevity-LLM fine-tune of LFM2; OpenMed made and published this conversion and is not affiliated with or endorsed by the model's authors.

Size and fidelity

Measured against the source model in FP32 on 4,092 tokens of public-domain prose (Project Gutenberg), ONNX Runtime 1.30.0, CPU:

The FP32 export this build was quantized from matches the source model (top-1 agreement 100%), so the difference above is the quantization. The width is higher than the "4-bit" label suggests because the tensors that lose most at 4 bits, and the shared embedding table, are kept at 8 bits. Weights exclude the rotary-position tables stored in the graph.

Quantization

The tokenizer, chat template and generation defaults are the upstream files, unchanged. Exact commands, tool versions and every file's SHA-256 are in openmed_build.json; the graph's metadata_props record the source revision and the modification notice.

Architecture

Quick start

Transformers.js (browser or Node)

import { pipeline } from "@huggingface/transformers";

const generator = await pipeline("text-generation", "OpenMed/LFM2-2.6B-Longevity-ONNX", { dtype: "q4" });
const messages = [
  { role: "user", content: "Which biomarkers in a routine blood panel say most about biological age, and why? /no_think" },
];
const output = await generator(messages, { max_new_tokens: 256, do_sample: false });
console.log(output[0].generated_text.at(-1).content);

ONNX Runtime (Python, Android and elsewhere)

The graph is a standard ONNX Runtime decoder: feed input_ids, attention_mask, position_ids and the past_conv.* / past_key_values.* cache inputs, then feed each present* output back as the matching past* input on the next step. It uses ONNX Runtime's com.microsoft operators (MatMulNBits, GatherBlockQuantized, GroupQueryAttention), so run it with ONNX Runtime 1.30 or later (onnxruntime-android on Android).

Tested with onnxruntime 1.30.0 (CPU execution provider) on macOS and with Transformers.js 4.3.0, which produced identical greedy tokens. It has not yet been measured on an Android device.

File set

Intended use

For research and education on aging biology. It is not a medical device and not a substitute for professional medical advice, diagnosis or treatment. Outputs can be wrong; verify them.

Licence

Distributed under the LFM Open License v1.0, the source model's licence. The ONNX graphs and weight files in onnx/ are modified files: OpenMed converted and quantized them from the source model's PyTorch weights on 2026-09-23. All other files are unchanged upstream copies.

Source: LiquidAI/LFM2-2.6B-Longevity at revision 05b12a8dc8a1e095038ae1a0a4524f8b21153916. Please cite the original model when you use this conversion.