Instructions to use anipanii/AskWise-PromptEngineer-1.5B-q4f16_1-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLC-LLM
How to use anipanii/AskWise-PromptEngineer-1.5B-q4f16_1-MLC with MLC-LLM:
# 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
metadata
license: apache-2.0
library_name: mlc-llm
tags:
- askwise
- prompt-engineering
- qwen2
- mlc
- webgpu
base_model: Qwen/Qwen2.5-1.5B-Instruct
AskWise-PromptEngineer-1.5B (MLC q4f16_1)
Fine-tuned Qwen2.5-1.5B-Instruct for AskWise Advanced rewrites:
messy user text → JSON {"structured","advanced"} prompt improvements (does not answer the task).
Use with AskWise Chrome extension
These are MLC WebLLM weights (q4f16_1). The extension reuses its packaged
Qwen2.5-1.5B WebGPU wasm — only this weight folder is downloaded from Hugging Face.
Converted with
- LoRA / QLoRA SFT on AskWise Instant seeds + synthetic prompt-engineering data
mlc_llm convert_weight … --quantization q4f16_1