LiteRT is Google's on-device runtime, the new name for TensorFlow Lite (Android: com.google.ai.edge.litert:litert), and litert-torch, the renamed ai-edge-torch, is its PyTorch converter: a PyTorch model converted unmodified with litert_torch.convert matched the original to 4e-7 on a Galaxy S26 (measured, LiteRT 2.2.0, Android 16, 2026-09-05).
LFM2.5-VL-1.6B — LiteRT-LM
LiquidAI/LFM2.5-VL-1.6B converted to the LiteRT-LM (.litertlm) format for on-device inference with Google's LiteRT-LM runtime — the middle of the family, between LFM2.5-VL-450M and LFM2.5-VL-3B.
Text + image work end-to-end on the released litert-lm 0.16.0 pip runtime: the bundle carries the vision encoder, the vision adapter and the LFM2 image-placeholder metadata, so litert-lm run … --attachment photo.png just works.
Known issue: positional answers are wrong on the released runtime
On litert-lm 0.16.0 — and on current main — only the top quarter of the image reaches the model. Captioning, colour questions and OCR of a large dominant subject still work. Anything positional — locate, count, enumerate, "which one is at the bottom" — comes back wrong, with no error and well-formed output.
The cause is a shrink-factor assumption in the runtime's vision path, reported upstream as LiteRT-LM#3246. It affects every LFM2.5-VL bundle, not just this one: the family performs its 2×2 pixel-unshuffle in the vision adapter rather than the encoder, and the runtime assumes the opposite.
Quick check — a 512×512 image with 16 numbered horizontal bands, asked to list every number from top to bottom:
expected: 1 … 16 actual: a runaway count that never stops at 16
The 1.6B degenerates rather than cutting off cleanly; the repaired build returns 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16.
A repaired int4 build is in this repo: LFM2.5-VL-1.6B_int4_fixB.litertlm — the stock bundle with its two vision graphs re-exported so the 2×2 pixel-unshuffle happens inside the encoder (text weights, tokenizer and metadata unchanged; scripts: reexport_vision_unshuffle.py + repack_vision.py). On the ruler above it returns 1 … 16 on litert-lm 0.16.1 and 0.17.0 (2026-09-16). It restores the visible area, not coordinate grounding — that stays a 3B capability in this family. The stock files stay published in case the runtime fix lands and makes them the right choice again.
LFM2.5-VL-1.6B pairs the hybrid LFM2 text backbone (16 layers: gated short-convolutions + grouped-query attention, hidden 2048, 64k vocab) with the same large SigLIP2 vision tower as the 3B (27 layers, hidden 1152). An image is processed at 512×512 into 256 soft tokens (single image per prompt; the runtime resizes for you) — but see the known issue above: on 0.16.0 those 256 tokens carry only the top quarter of the picture.
Verified on a Pixel 8a (Android 16, LiteRT-LM 0.16.1, 2026-09-07): LFM2.5-VL-1.6B_int4.litertlm, downloaded from this repository and checked against its SHA-256, answered one text turn and one image question with the language model on the GPU and the vision encoder on the CPU, and again with both on the CPU.
| File | Recipe | Size |
|---|---|---|
LFM2.5-VL-1.6B_int8.litertlm | int8 dynamic (text linears + convs + embedding, vision tower) | 1.81 GB |
LFM2.5-VL-1.6B_int4.litertlm | text int4 blockwise-32 OCTAV linears, int8 embedding + lm_head; vision tower int8 | 1.30 GB |
LFM2.5-VL-1.6B_int4_fixB.litertlm | int4 as above, vision graphs re-exported with the pooling inside the encoder (full image visible on 0.16.x/0.17.0) | 1.30 GB |
| Context (KV cache) | 4096 max |
| Image input | 1 per prompt, resized to 512×512 → 256 tokens; PNG/JPEG via --attachment . On 0.16.0 those tokens cover only the top quarter — see the known issue |
| Backend | CPU, and GPU with litert-lm ≥ 0.16.0 (macOS and Android OpenCL measured below; iOS Metal fails at engine creation for this family, tracked upstream in LiteRT-LM#3129 — use CPU on iOS) |
| Template | bundled — ChatML-style; image placeholders are inserted by the runtime's LFM2 data processor (non-thinking model) |
| Base model | LiquidAI/LFM2.5-VL-1.6B (LFM Open License v1.0) |
Quality
Sanity gates on the 0.16.0 pip CLI (greedy, fresh engine per question, --cache no). Vision: five deterministic synthetic fixtures (dominant color, large-text OCR, shape, counting three squares, largest word). Text: the 8-question gate used across our LiteRT conversions.
| Configuration | text 8Q | image 5Q |
|---|---|---|
| PyTorch bf16 (reference) | — | 5/5 |
| LiteRT int4-b32 (cpu & gpu) | 8/8 | 3/5 |
| LiteRT int8 (cpu & gpu) | 8/8 | 3/5 |
Text is perfect across every configuration and backend. On the image side, color, large-text OCR and largest-word are answered correctly, and additional geometry probes (which corner an object is in, horizontal-vs-vertical stripes) also come back correct — but the fine-grained shape and counting fixtures miss on-device across all quantization levels including an unquantized probe build, while the PyTorch reference gets them right. We verified the conversion itself is exact (the exported vision tower and projector match PyTorch at cosine 1.0000 on identical inputs, and the prompt/token stream matches the HF processor token-for-token), so this is a runtime effect, not conversion loss. Correction (2026-08-14): we have since identified it — it is the defect described in the known issue above, and the shape and counting fixtures sit below the visible quarter. The 3B scores 5/5 on these fixtures despite being affected identically, because a larger model answers them from global context rather than from the pixels. Do not read the 3B's 5/5 as evidence that it sees more of the image.
Usage
pip install litert-lm
litert-lm run ./LFM2.5-VL-1.6B_int4.litertlm --prompt "What does the text in this image say?" --attachment photo.png
Text-only prompts work the same way without --attachment. --vision-backend cpu|gpu selects the vision encoder backend independently of the text backend.
One Engine per conversation. On litert-lm 0.18.0 the runtime starts a new Conversation on an Engine that already ran another conversation from the earlier conversation's short-convolution state: it reuses the prompt tokens the two conversations share and does not reset the state (my open report). In a probe that asks three questions (What is the capital of France? / What is 17 times 23? / What color is the sky on a clear day?) first in a pirate-persona conversation and then in a conversation told to answer in one word, on the same Engine against a fresh Engine in a new process (answers capped at 200 tokens; CPU backend; litert-lm 0.18.0 on a Mac), the _int4, _int4_fixB and _int8 files each gave the same three one-word answers on both Engines, in its one run. Those three short questions say nothing about longer conversations or image turns. Create a new Engine for each conversation. The first conversation on an Engine is unaffected.
Speed
litert-lm benchmark … --cache no, litert-lm 0.16.0 pip, Apple M4 Max (128 GB), text path (image encoding is a separate one-shot vision-encoder call at prompt time):
CPU backend:
| Variant | Prefill (256) | Prefill (1024) | Decode | TTFT |
|---|---|---|---|---|
| int8 | 365 tok/s | 796 tok/s | 70.5 tok/s | 0.72 s |
| int4 | 329 tok/s | 405 tok/s | 78.2 tok/s | 0.79 s |
GPU backend (--backend gpu; both variants verified to generate on GPU before quoting):
| Variant | Prefill (256) | Decode | TTFT |
|---|---|---|---|
| int8 | 3601 tok/s | 221.5 tok/s | 0.08 s |
| int4 | 3792 tok/s | 275.3 tok/s | 0.07 s |
On Android the same bundles run GPU-accelerated. Pixel 8a (Tensor G3), litert_lm_main built from the v0.16.0 release tag, 296-token prompt, decode run to EOS (1.5k–3.8k tokens sustained), --disable_cache:
| Variant | Backend | Prefill (296 tok) | Decode | TTFT |
|---|---|---|---|---|
| int4 | GPU (OpenCL) | 201 tok/s | 22.1 tok/s | 1.5 s |
| int4 | CPU | 37 tok/s | 13.6 tok/s | 8.1 s |
| int8 | GPU (OpenCL) | 403 tok/s | 15.9 tok/s | 0.80 s |
| int8 | CPU | 60 tok/s | 8.4 tok/s | 5.0 s |
The text graph delegates fully on Android OpenCL (543/543 nodes, zero rejected ops). As with the 450M, int4 prefills slower than int8 on CPU (blockwise-int4 repacking) but decodes markedly faster — pick by whether your prompts or your outputs dominate.
Galaxy S26 — GPU backend
Both published bundles run on the Android GPU backend and generate.
| file | GPU backend | delegation | peak |
|---|---|---|---|
LFM2.5-VL-1.6B_int4.litertlm | runs | 6443 / 6443 ops across 12 subgraphs on LiteRT GPU | 792 MB |
LFM2.5-VL-1.6B_int8.litertlm | runs | 6443 / 6443 ops across 12 subgraphs on LiteRT GPU | 721 MB |
Measured on a Samsung Galaxy S26 (SM-S942Q / SM8850, Android 16) with litert_lm_advanced_main from litert-lm 0.16.0, --backend=gpu --sampler_backend=cpu, prompt What is the capital of France?. Peak is the process high-water mark (VmHWM) sampled during that same run. Gated 2026-08-25.
The op counts above are the LiteRT GPU partitions. XNNPACK additionally takes 1 of the 4 nodes in main; the runtime accepts that split.
The gate prompt carries no image, so this covers engine creation and the text path. The vision path on the GPU is not measured here.
No speed rows, on purpose. On this handset the GPU backend wins prefill and does not win decode, so a GPU throughput figure only means something beside a CPU row from the same handset, and no S26 CPU row exists for this model yet.
GPU wiring, including the Gallery import toggle: GPU guide.
Conversion
Converted with the open pipeline in hf-to-litertlm (lfm_work/convert_lfm25_vl.py, litert-torch 0.9.3 --task image_text_to_text): the exact recipe, the int4 post-processing (OCTAV int4-b32 + int8 embedder + zero-scale repair + executor metadata, all vision sections preserved) and the text/image gate harnesses are in the repo's REPRODUCE.md.