Qwen/Qwen3-4B-Thinking-2507 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).

Qwen3-4B-Thinking-2507 is a dense 4B reasoning model (Qwen3ForCausalLM, 36 layers) that operates exclusively in thinking mode — it emits a <think>…</think> chain before its answer — so it rides the existing Qwen3 converter and runtime directly.


File	`model.litertlm` — int4 block 128 (~2.3 GB)
Quantization	int4 weights (symmetric) + OCTAV optimal-clipping; embeddings INT8 (externalized section)
Compute	integer
Context (KV cache)	4096
Base model	Qwen/Qwen3-4B-Thinking-2507
Decode speed	~14 tok/s (iPhone 17 Pro, GPU) · ~67 tok/s (Mac M-series, GPU)

⚠️ It's a reasoning model — give it room to think

This model generates a <think>…</think> reasoning chain, then the answer. Run it with max_tokens ≥ 2048 — at a short limit it gets cut off mid-thought and never reaches the answer. (All quality numbers below were measured at 2048.)

Quality — GSM8K parity

Measured on GSM8K (n=100, greedy, 0-shot chain-of-thought, max_tokens 2048, identical prompt and answer-extraction for every row).

Configuration	GSM8K
bf16 (reference)	90.0%
LiteRT int4 — block 128	86.0% (−4 pt)

int4 is at parity (−4 pt). Note: evaluating a reasoning model at a short token budget badly understates int4 — the longer int4 reasoning chains get truncated before the answer; benchmark reasoning models with max_tokens ≥ 2048.

Why block 128 (and not block 32)? For this reasoning model the block-32 build degraded more (−9 pt) and produced corrupted output under the iPhone GPU delegate, while block 128 is robust on every backend, ~40 % faster to decode (¼ the dequant scales — which matters when generating long <think> chains), and stays at −4 pt parity. So only the block-128 build is published.

Usage

bash

# 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?"

Run on Android

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 adb needed. 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:

Install a recent Gallery (package com.google.ai.edge.gallery, 1.0.15+ supports .litertlm).
Download model.litertlm and push it: adb push model.litertlm /sdcard/Download/
In the app tap +, pick the file, choose the GPU backend, and raise the max-tokens setting (≥2048).
Chat — the bundle already carries the tokenizer and Qwen3 chat template.

A 4B int4 build needs ~2.5 GB free RAM; reboot the phone first if memory is tight.

Run on desktop (LiteRT-LM CLI)

The same .litertlm bundle runs on macOS / Linux / Windows with the official LiteRT-LM CLI — including as a local OpenAI-compatible API server:

bash

pip install litert-lm
litert-lm import --from-huggingface-repo litert-community/Qwen3-4B-Thinking-2507 model.litertlm qwen3-4b-thinking-2507
litert-lm run qwen3-4b-thinking-2507     # interactive chat in the terminal
litert-lm serve           # local OpenAI-compatible API server

Run on iPhone

Verified on iPhone 17 Pro (LiteRT-LM Swift runtime): loads and generates at ~14 tok/s.

Conversion

Converted with the official litert-torch converter — a standard Qwen3ForCausalLM, so it uses the existing Qwen3 path with no custom graph code. Recipe: blockwise-128 int4 + OCTAV (INT4 weights, block 128, symmetric, OCTAV optimal-clipping), embeddings INT8, KV cache 4096.

python

from litert_torch.generative.export_hf.export import export
export(
    model="Qwen/Qwen3-4B-Thinking-2507",
    output_dir="out",
    quantization_recipe="qwen3_int4_block128_octav.json",  # blockwise-128 int4 + OCTAV, int8 embeddings
    cache_length=4096,
    externalize_embedder=True,
)

License

Apache-2.0, inherited from the base model Qwen/Qwen3-4B-Thinking-2507.

kinkiller/Qwen3-4B-Thinking-2507

Qwen3-4B-Thinking-2507 — LiteRT-LM (blockwise int4)