daveycodez/LFM2.5-1.2B-Web

LFM2.5-1.2B-Instruct ONNX for the web

A 4-bit ONNX build of LFM2.5-1.2B-Instruct by Liquid AI, made for in-browser use with Transformers.js on WebGPU, including iPhone Safari.

This is an unofficial community build. It is not made, reviewed or endorsed by Liquid AI.

Changes from the original

This repository contains a modified file. Compared with LiquidAI/LFM2.5-1.2B-Instruct-ONNX at commit 10f72e7 (onnx/model_q4f16.onnx):

• Logits only for the positions you need: the graph has a new num_logits_to_keep input (int64 scalar), the same input onnx-community/LFM3-1.7B-ONNX has. The final-norm hidden states are sliced to the last num_logits_to_keep positions before the LM head; 0 keeps every position, as before. Transformers.js passes 1 during generate(), so reading a prompt computes one row of logits instead of one per prompt token. The logits output is [batch, num_logits_to_keep, 65536], or [batch, sequence_length, 65536] for 0.
• Why: the original graph returns float32 logits for every prompt token. For an 800-token prompt that is a 210 MB tensor (plus a 105 MB float16 one before the cast), made on the GPU and then copied to the page. On iPhone Safari, where a tab gets about 2 GB including GPU memory, long prompts such as ones with a tool definition crashed the tab.
• Nothing else changed: the edit adds three nodes (Neg, Unsqueeze, Slice), three small int64 constants and the input. The weights are not touched: onnx/model_q4f16.onnx_data is byte-identical to the original (SHA-256 46cfacc12941150620a3f644a5269e9baebd75d681cfa09cadeef71b8ed64ac2).

config.json, generation_config.json, tokenizer.json, tokenizer_config.json and chat_template.jinja are unchanged copies. Only the q4f16 model is included.

	Original q4f16	This build
logits for an 800-token prompt	[1, 800, 65536], 209,715,200 bytes	[1, 1, 65536], 262,144 bytes
Last-token logits on 5 prompts of 18–963 tokens, max abs difference		≤ 0.0078 (logits up to ±17), same top token on every prompt
All positions (num_logits_to_keep = 0), max abs difference		0
KV and conv caches after the prompt		identical
8 greedy decode steps after each prompt		identical tokens and logits
541-token tool prompt + 24 new tokens, Transformers.js 4.3.2 on WebGPU (Node, Apple Silicon)	250–259 ms	222–228 ms
963-token prompt, onnxruntime CPU	1,669 ms	1,497 ms

Measurements were taken with onnxruntime 1.31 on CPU unless noted. The small last-token difference comes from the LM head's 4-bit matmul running on one row instead of hundreds, which changes the order of the float16 sums. Through Transformers.js 4.3.2, greedy generation gave the same tokens with both builds.

Usage

Use it like the original ONNX model, with dtype: "q4f16" and device: "webgpu". See LiquidAI/LFM2.5-1.2B-Instruct-ONNX for examples. Transformers.js sets num_logits_to_keep on its own. If you run the graph with onnxruntime directly, pass it as an int64 scalar: 1 for generation, 0 for all positions.

License

LFM2.5 is released under the LFM Open License v1.0, which also covers this build. Commercial use is licensed only for entities with annual revenue under $10 million. Original model © Liquid AI, Inc.