Instructions to use ChaoticRay/cookxpert-chef-1.7b-instruct-mlc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLC-LLM
How to use ChaoticRay/cookxpert-chef-1.7b-instruct-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
CookXpert Chef 1.7B (MLC)
CookXpert chef fine-tune MLC bundle for on-device WebGPU inference.
Weights are mirrored from mlc-ai/SmolLM2-1.7B-Instruct-q4f16_1-MLC until custom LoRA MLC artifacts are uploaded. The app applies CookXpert chef system prompts on top of these weights.
Replace with custom fine-tune
export HF_TOKEN=hf_...
python ml/cookxpert_chef_ft/publish_to_hf.py --config ml/cookxpert_chef_ft/config.gpu.yaml --create-repo
When mlc-chat-config.json and wasm weights in this repo match your build, CookXpert shows Weights live.
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Model tree for ChaoticRay/cookxpert-chef-1.7b-instruct-mlc
Base model
HuggingFaceTB/SmolLM2-1.7B Quantized
HuggingFaceTB/SmolLM2-1.7B-Instruct