No official torrent yet — requesting it tells the flock what to host next. You cannot seed a catalog-only page.
Cataloged from litert-community/Qwen3-1.7B
All models by litert-community →This repository contains LiteRT-LM variants of Qwen/Qwen3-1.7B optimized for on-device text generation.
Qwen3-1.7B_dynamic_wi4b32_afp32.litertlm is a dynamic INT4 variant (block-32 weights, FP32 activations). It was converted through the LiteRT Torch (litert-torch) path and quantized with AI Edge Quantizer. This artifact incorporates LiteRT-LM GPU graph optimizations, including composite ops for RoPE, fused QKV, and fused Gate/Up projections, and is configured with static prefill memory allocation.
.litertlm model and click submit. The model will then appear in your list, and you can proceed to download it (a Hugging Face account login is required)..litertlm model directly to your Android device, OR download it to your computer and push it via ADB (e.g., adb push Qwen3-1.7B_dynamic_wi4b32_afp32.litertlm /sdcard/Download/). Then, select this option, choose the downloaded file from your storage, configure your preferred parameters, and tap "Import".For full details on importing models and other features, see the Edge Gallery App Wiki.
To build the demo app from source, please follow the instructions from the GitHub repository.
Community measurement on a physical Samsung Galaxy S26 (SM-S942Q, Snapdragon 8 Elite Gen 5 / SM8850, Android 16): litert_lm_advanced_main from the litert-lm v0.16.0 release, GPU backend OpenCL (LITERT_CL), one fixed 205-token prompt text (202 tokens under this tokenizer), --benchmark. Two runs per backend taken back-to-back — cells show the range (the phone warms across a series; the CPU's first run also pays cold file I/O). Peak RSS is the process VmHWM. Before quoting, every file/backend pair was run with a real prompt and produced a correct text answer; the GPU rows are full delegation (e.g. Qwen3-1.7B_dynamic_wi4b32_afp32: decode 994/994 ops on LITERT_CL, nothing on the CPU fallback).
What the table says:
Qwen3_1.7B.litertlm prefills 3–5× faster than wi4b32 on the same CPU.Ready to integrate this into your product? Get started in the LiteRT-LM documentation.
pip install litert-lm litert-lm run --from-huggingface-repo=litert-community/Qwen3-1.7B Qwen3_1.7B.litertlm --prompt="Write me a poem on nature" pip install litert-lm huggingface_hub from huggingface_hub import hf_hub_download
model_path = hf_hub_download( repo_id="litert-community/Qwen3-1.7B", filename="Qwen3_1.7B.litertlm" ) import litert_lm
litert_lm.set_min_log_severity(litert_lm.LogSeverity.ERROR) # Hide log for TUI app
with litert_lm.Engine(model_path) as engine: with engine.create_conversation() as conversation: while True: user_input = input("\n>>> ") for chunk in conversation.send_message_async(user_input): print(chunk["content"][0]["text"], end="", flush=True)