Retail Bank Agent 9B
This is a research checkpoint for a synthetic retail-bank customer-service
demonstration. It is a bf16-lora LoRA adapter (rank 32, alpha 64) trained on top of spkc83/retail-bank-servicing-agent-9b at revision 1d56824995aa1adecfe20f62ca42fb1c0c443817; the base has approximately 8.8 billion parameters and uses its native tagged JSON tool-call format.
Training
- Dataset:
spkc83/retail-bank-servicing-alignment-sft at e9d67b3d6183f216f51d664fc53983f432e664ef
- Training records: 3467
- Validation records: 421
- Tool manifest: nine synthetic retail-banking tools
- Assistant-only target masking: tool-call and final-assistant spans
- Maximum sequence length: 2048
- Optimizer steps: 2000
- LoRA rank/alpha: 32/64
- Source commit:
444579796024bfe585c5ae18ef3bd4e86c580d62
- Chat-template SHA-256:
6727ca16a39df05c41af54eb651aa618b50a29967ad3951a31b90c4e385573fc
The repository root is the trained BF16 LoRA adapter only: there are no merged
weights and no config.json. Load it with PeftModel.from_pretrained(base, repo, revision=...) on top of the base model at the pinned revision. The same adapter is
duplicated under adapter/.
Intended use and limitations
The model is intended only for the linked synthetic banking POC. It must be
given the published tool schemas and tool results. It has no access to real
banking systems, is not financial advice, and may make incorrect tool choices
or unsupported claims. Evaluate tool-call syntax, arguments, backend
execution, grounded final responses, OOD behavior, and multi-turn behavior
before relying on a revision.
