Instructions to use queensone/stockforge-router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use queensone/stockforge-router 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
- LiteRT-LM
How to use queensone/stockforge-router 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=queensone/stockforge-router \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
StockForge question router (Qwen3-0.6B, LiteRT INT4)
The on-device model behind the StockForge inventory app's assistant. It turns a shop owner's question in English, Spanish or Portuguese into one lookup, as one line of JSON:
¿qué vendió Carlos ayer? -> {"intent": "stock_movements", "args": {"hours": 48, "person": "Carlos", "reason": "sale"}}
It never answers with data itself: the app sends the lookup to its server, which answers from the user's own records.
- Base: Qwen/Qwen3-0.6B, full fine-tune on ~1.8k synthetic examples
- Format:
.litertlmfor LiteRT-LM, dynamic INT4 (block 32), 1280-token context - System prompt:
Route the shop inventory question to one lookup. Reply with one JSON line.(thinking off) - Held-out test (120 questions, 40 per language): 98% correct lookup, 95% lookup + arguments; median 1.1 s on a phone CPU
License: Apache 2.0, as the base model.
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