Instructions to use build-small-hackathon/hackathon-advisor-quest-minicpm5-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use build-small-hackathon/hackathon-advisor-quest-minicpm5-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-1B") model = PeftModel.from_pretrained(base_model, "build-small-hackathon/hackathon-advisor-quest-minicpm5-lora") - Notebooks
- Google Colab
- Kaggle
Hackathon Advisor — Quest Classification LoRA (MiniCPM5-1B)
PEFT LoRA adapter that classifies a Build Small Hackathon project against 13 judging dimensions (6 merit badges + 2 tracks + 5 sponsor/special awards) from a two-segment README + app-file prompt, emitting strict JSON:
{"matches":[{"quest":"...","confidence":0.0,"evidence":"...","source":"readme|app_file"}]}
Load it in the deployed Space by setting ADVISOR_QUEST_ADAPTER_ID to this repo.
The backend revalidates every dashboard refresh and will not swap on schema failure.
Recipe
- Base model:
openbmb/MiniCPM5-1B - Task:
hackathon_advisor_quest_classification - Method: LoRA SFT (completion-only loss)
- Examples: 259
- Epochs: 16.0
- LoRA rank/alpha/dropout: 64/128/0.0
- Max seq length: 3072
- GPU: L40S
Dataset
build-small-hackathon/hackathon-advisor-quest-dataset — 156 chat-JSONL examples built from real build-small-hackathon Spaces: 108 teacher-
labelled + adversarially-verified projects plus targeted augmentations (app-only,
readme-only / missing app file, README↔app contradictions, empty matches, noisy
metadata). All 13 quests covered.
Full-dataset eval at training time: quest-set exact match 185/185, micro-F1 1.0.
Evaluated by reproducing the gold quest set for every example in the training dataset
(the dataset is the spec — it is built from the real build-small-hackathon projects).
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Model tree for build-small-hackathon/hackathon-advisor-quest-minicpm5-lora
Base model
openbmb/MiniCPM5-1B