litert-community / Polaris-4B-Preview

Polaris-4B-Preview — LiteRT-LM (blockwise int4)

POLARIS-Project/Polaris-4B-Preview 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).

Polaris-4B is an RL post-trained reasoning model built on Qwen3-4B (standard dense qwen3 , Apache-2.0). It is tuned for hard competition math and works the problem inside a <think>…</think> chain before answering — a SOTA-for-size math reasoner that runs fully on a phone.

What it's good at — hard math (AIME)

Polaris-4B's headline is competition math. Per the base model card, at ~4B params it reports AIME24 81.2 / AIME25 79.4 , in the range of far larger frontier reasoners. It is optimized for long-chain hard-problem reasoning rather than grade-school arithmetic — give it a generous token budget (it thinks at length).

Usage

litert_lm_main \
  --model_path model.litertlm \
  --backend gpu \
  --input_prompt "Find the number of ordered pairs (a,b) of integers with 1<=a,b<=100 such that a*b is a perfect square."

The .litertlm bundle carries the tokenizer and a ChatML prompt template ( <|im_start|>role\n … <|im_end|> ). It emits a <think>…</think> chain then the final answer, and stops cleanly at <|im_end|> . Set a high max-tokens (≥2048) — a reasoning model truncated mid-thought produces no answer.

Run on Android

Install a recent Google AI Edge Gallery (1.0.16+ imports .litertlm directly from Hugging Face), import this repo (or push model.litertlm ), pick the GPU backend, and chat. It's a ~2.3 GB / 4B model — GPU needs a ~12 GB+ device; free RAM first on smaller phones.

Quality — GSM8K (on-device int4 parity)

Measured on GSM8K (n=50, greedy, 0-shot chain-of-thought, max-tokens 2048 ):

Non-degenerate, passes the local quality gate 8/8 with a clean stop at <|im_end|> . GSM8K undersells this model — it is tuned for AIME-level problems, and on easy arithmetic its long exploratory reasoning is not where its edge shows. block128 is used (rather than block32) because a 4B reasoning model's block32 weights can corrupt on the iPhone Metal GPU; block128 loads and runs stably across iPhone / Android / desktop.

Conversion

Converted with litert-torch : blockwise int4 (block 128) + OCTAV optimal-clipping, embedding INT8, KV cache 4096, ChatML template. Polaris-4B is a standard dense Qwen3ForCausalLM (with rope_scaling: yarn , exported with a cache within original_max_position_embeddings so base RoPE is exact), so it rides the existing Qwen3 converter with no custom graph code. externalize_embedder=True keeps every .litertlm section under the iOS ~2 GiB single-section mmap limit so it loads on iPhone.

License

Apache-2.0, inherited from the base model POLARIS-Project/Polaris-4B-Preview .