retail_banking-rag_qa-v0
LoRA adapter for retail_banking (rag_qa). answer retail banking customer servicing questions grounded in the bank's policy and product documents
What this is
A demonstration adapter from the SLM Suite: trained on a fictional policy corpus (no real customer or institutional data), promoted through gated evaluations (retrieval_recall, citation_correct, groundedness, key_fact_match, assistive_refusal, no_regression), and assistive by training — it cites its sources and refuses to make decisions for the user (refusal behavior is a measured gate, not a prompt).
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained('Qwen/Qwen2.5-7B-Instruct')
model = PeftModel.from_pretrained(base, 'mjayason/retail-banking-servicing-qa-lora')
tok = AutoTokenizer.from_pretrained('Qwen/Qwen2.5-7B-Instruct')
Prompt shape: a Context: block of policy snippets, then the question — answers cite the [POLICY-ID]s present in the context.
Limits
The corpus is fictional and small; this is a capability demonstration, not a production system. Do not rely on it for real financial decisions.
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