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Cataloged from litert-community/VibeThinker-3B
All models by litert-community →WeiboAI/VibeThinker-3B converted to the
LiteRT-LM (.litertlm) format for on-device inference with Google's
LiteRT-LM runtime (the engine behind the official
litert-community/* models).
VibeThinker-3B is a dense 3B math/reasoning model (Qwen2ForCausalLM, 36 layers) — it solves
problems with an inline chain-of-thought and is strong at arithmetic and math word problems. Standard
Qwen2 architecture, so it rides the existing converter and runtime directly.
VibeThinker solves with a step-by-step chain-of-thought, then a \boxed{} answer. Run it with
max_tokens ≥ 2048 — at a short limit it gets cut off before the answer. (All quality numbers
below were measured at 2048.)
litert-lm benchmark (litert-lm 0.15.0) on an Apple M4 Max, -p 256 -d 256 --runs 3 (the tool averages three iterations), max-num-tokens 4096, warm-up run discarded, otherwise idle machine.
Reproducibility: the GPU rows repeat to within about 1% across invocations; the CPU rows are noisier — re-running the 1B control six times spread its CPU decode over 29.0–33.3 tok/s, so treat the CPU column as accurate to roughly ±7%.
The model loads and generates on an iPhone 17 Pro GPU, but no on-device timing was taken, so none is quoted.
Measured on GSM8K (n=100, greedy, 0-shot chain-of-thought, max_tokens 2048, identical prompt and answer-extraction for every row).
int4 (block 32) is at parity (−7 pt) and still 90% — strong for an on-device math model. bf16's 97% reflects this model's math specialization.
Why block 32 (not block 128)? This is a precision-sensitive math model: the coarser block-128 int4 (¼ the dequant scales) collapsed to 64% (−33 pt) on GSM8K, while block 32 holds at 90%. So only the block-32 build is published. (Note: for general-purpose 4B reasoning models the opposite holds — block 128 is fine and faster — but exact arithmetic needs the finer block-32 grid.)
The published bundle runs on the Android GPU backend and generates.
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 2026-08-24.
The op counts above are the LiteRT GPU partitions. XNNPACK additionally takes 1 of the 4 nodes in decode_embedder and 1 of the 4 nodes in prefill_embedder_128; the runtime accepts that split.
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.
The .litertlm bundle carries the tokenizer and prompt template (Qwen2 ChatML —
<|im_start|>role\n…<|im_end|>), so no separate tokenizer files are needed.
Update (July 2026): Google AI Edge Gallery v1.0.16+ can import litert-lm models directly from Hugging Face inside the app (tap +) — no computer or
adbneeded. The manual steps below are only required on older builds or for sideloading a local file.
The official Google AI Edge Gallery app runs
.litertlm models on-device:
com.google.ai.edge.gallery, 1.0.15+ supports .litertlm).model.litertlm and push it: adb push model.litertlm /sdcard/Download/Measured on an 8 GB phone (added 2026-08-17): driving litert_lm_main directly on a Pixel 8a (Tensor G3, Mali-G715, 8 GB RAM), the graph runs entirely on the OpenCL delegate — 1603/1603 nodes in the 128-token prefill graph and 1452/1452 in decode, zero rejected ops. Worth knowing when reading its answers: this is a math-specialised reasoning model, so on general-knowledge prompts it reasons at length and can settle on a wrong answer — identically on CPU and GPU, which is how you can tell it is the model's domain rather than the backend.
The same .litertlm bundle runs on macOS / Linux / Windows with the official
LiteRT-LM CLI — including as a
local OpenAI-compatible API server:
Verified on iPhone 17 Pro (LiteRT-LM Swift runtime): the block-32 build (1.62 GiB section, under the iOS limit) loads and generates correct answers.
Converted with the official litert-torch
converter — a standard Qwen2ForCausalLM, no custom graph code. Recipe: blockwise-32 int4 + OCTAV
(INT4 weights, block 32, symmetric, OCTAV optimal-clipping), embeddings INT8, KV cache 4096.
The upstream chat template emits a default system turn whenever the caller sends no system message — for this model: You are a helpful assistant.. The converter's template probe renders the template with a system message already present, so that block was never seen and never reached the bundle: with no system message the model was running without the default system turn it was tuned with. The chat template in model.litertlm now emits the block exactly once when no system message is given. In model.litertlm, the block is not emitted when you pass a system message. The restored block adds 11 prefill tokens to a conversation that sends no system message, so time-to-first-token grows by that much; per-token speed is unchanged.
Metadata-only change: every section of the bundle except the LlmMetadata block is byte-identical to the previous file (verified by sha256 per section), so the weights, the graph and the tokenizer are unchanged and the speed and memory numbers on this card still describe exactly this file per token — only the file's own sha256 differs. What changed is the input: with no system message, the prompt now renders byte-identical to the upstream chat template's output, verified on the LiteRT-LM runtime. A system message you pass yourself renders as before. Greedy decoding can turn on a single token, so an individual answer can differ from the previous file. Unless a row says otherwise, the accuracy figures on this card were measured on the previous file and have not been re-measured on this one. If you downloaded before 2026-08-29, re-download.
MIT, inherited from the base model WeiboAI/VibeThinker-3B.
# build litert-lm from https://github.com/google-ai-edge/litert-lm, then:litert_lm_main \ --model_path model.litertlm \ --backend gpu \ --input_prompt "A bat and a ball cost \$1.10. The bat costs \$1.00 more than the ball. How much is the ball?"pip install litert-lmlitert-lm import --from-huggingface-repo litert-community/VibeThinker-3B model.litertlm vibethinker-3blitert-lm run vibethinker-3b # interactive chat in the terminallitert-lm serve # local OpenAI-compatible API serverfrom litert_torch.generative.export_hf.export import exportexport( model="WeiboAI/VibeThinker-3B", output_dir="out", quantization_recipe="qwen3_int4_block32_octav.json", # blockwise-32 int4 + OCTAV, int8 embeddings cache_length=4096, externalize_embedder=True,)