Instructions to use litert-community/Qwen3-4B-Thinking-2507 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/Qwen3-4B-Thinking-2507 with LiteRT-LM:
# LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM) # and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter). # For platform-specific integration guides, please refer to the official developer website: # https://ai.google.dev/edge/litert-lm # To try LiteRT-LM, the easiest way is to use our CLI tool. # 1. Install the LiteRT-LM CLI tool: pip install -U litert-lm # 2. Download and run this model locally: # See: https://ai.google.dev/edge/litert-lm/cli # A single .litertlm file in the repo is picked automatically; otherwise the CLI asks which one to run # (or pass its name right after the repo id). litert-lm run \ --from-huggingface-repo=litert-community/Qwen3-4B-Thinking-2507 \ --prompt="Write me a poem"
- LiteRT
How to use litert-community/Qwen3-4B-Thinking-2507 with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
GPU graph re-ship: KV-cache update and attention rewritten into the runtime's GPU composite ops plus a 16-token prefill signature; weights, tokenizer and metadata byte-identical; CPU output bit-identical (card and manifest updated)
f8ce479 verified Download model.litertlm from litert-community/Qwen3-4B-Thinking-2507: direct link, hf CLI and curl.
- Browser
- Download file 2.48 GB
-
https://huggingface.co/litert-community/Qwen3-4B-Thinking-2507/resolve/main/model.litertlm
- Command line
-
hf download hf://litert-community/Qwen3-4B-Thinking-2507/model.litertlm
-
curl -L -o model.litertlm https://huggingface.co/litert-community/Qwen3-4B-Thinking-2507/resolve/main/model.litertlm
2.48 GB
- Xet hash:
- 7e0e3aeb8754a166de16fa3b7743fac36116fdbe151386ac30ae2fd5469d714e
- Size of remote file:
- 2.48 GB
- SHA256:
- 8a8a6a419a5570b3f4814d358fb795c0dcb6931206c7dae29480f47564c3f04d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.