Foresight AI DeFi Wallet Risk Classifier
This repository contains the AutoScientist-trained LoRA adapter for Foresight AI, a DeFi wallet risk review system built for the Adaption Labs AutoScientist Challenge in the finance category.
The adapter was trained to classify whether a DeFi wallet behavior sample indicates a higher-risk state within a 14-day horizon. The expected completion is a binary label:
0means lower-risk outcome1means higher-risk outcome
Training Run
- Fine-tune job ID:
351792b9-5320-432e-ac12-889388d6b47a - Training experiment ID:
3d6e6540-6138-4890-b097-c29deb8e580f - Base model:
meta-llama/Llama-4-Scout-17B-16E-Instruct - Training method: SFT
- Adapter type: LoRA
- Data format: chat
- Epochs: 3
- LoRA rank: 64
- LoRA alpha: 128
- Learning rate: 0.0001
- Scheduler: cosine
- Final exported eval loss: 1.142578125
Dataset
The model was trained on the Foresight AI DeFi wallet risk instruction dataset. The hackathon dataset contains 1,000 instruction rows produced from retrospective wallet behavior samples.
Important: these labels are retrospective proxy labels used for hackathon model development. They should not be described as production-verified liquidation or drawdown outcomes.
Intended Use
This adapter is intended for research and demonstration of DeFi wallet risk classification. It can support a user-facing wallet review workflow where outputs are presented as decision-support signals rather than financial advice.
Limitations
- This is not a production financial risk model.
- The training labels are retrospective proxy labels, not independently verified future outcomes.
- The model should not be used to execute trades, move funds, or make automated liquidation or lending decisions.
- The model does not prove protocol exposure, health factor, leverage, or liquidation risk unless those facts are supported by external on-chain data.
- Outputs should be paired with Foresight AI's evidence-aware wallet detector, provider status, and data quality checks.
Adapter Files
This export contains PEFT/LoRA adapter weights and tokenizer/config files:
adapter_model.safetensorsadapter_config.jsontokenizer.jsontokenizer_config.jsonspecial_tokens_map.jsonchat_template.jinjatrainer_state.json
License And Base Model Terms
Use of this adapter is subject to the license and acceptable-use terms of the base model and any Adaption Labs challenge requirements.
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Base model
meta-llama/Llama-4-Scout-17B-16E
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("togethercomputer/Llama-4-Scout-17B-16E-Instruct_bnb_4bit") model = PeftModel.from_pretrained(base_model, "samscript18/adaption_defi_wallet_risk_classif_3d6e6540")