Instructions to use raining-codes/Qwen3-1.7B-LOMO-q4f16_1-MLC2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLC-LLM
How to use raining-codes/Qwen3-1.7B-LOMO-q4f16_1-MLC2 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
Qwen3-1.7B-LOMO-q4f16_1-MLC2
MLC-LLM formatted weights for on-device inference (Android / Vulkan, CPU, etc.).
- Base model:
Qwen/Qwen3-1.7B - Quantization:
q4f16_1(group size 32, int4 weights + f16 scales) - Conversation template:
chatml← match runtime prompt formatting with training - Files
mlc-chat-config.jsonparams_shard_*.bintensor-cache.json- tokenizer files (
tokenizer.json+vocab.json+merges.txt)
Quick test (CLI)
mlc_llm chat HF://raining-codes/Qwen3-1.7B-LOMO-q4f16_1-MLC2 --temperature 0.7 --top-p 0.9 --max-gen-len 256
Notes
- This repo contains MLC execution format. It is not directly loadable with HF Transformers.
- In apps (e.g., MLCChat for Android), use "Add remote model" with
raining-codes/Qwen3-1.7B-LOMO-q4f16_1-MLC2.
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