license: llama3.2
base_model: unsloth/Llama-3.2-3B-Instruct
library_name: peft
tags:
- lora
- peft
- email-triage
- tool-calling
- text-classification
pipeline_tag: text-generation
Email-Triage LoRA (Llama-3.2-3B-Instruct)
A LoRA adapter (r=16) fine-tuned via Behavioral Cloning to triage corporate emails by
selecting one of three tools — route_to_human, auto_reply, or ask_for_clarification —
returned as a strict JSON tool call.
- Base model:
unsloth/Llama-3.2-3B-Instruct - Method: Behavioral Cloning (Unsloth + Hugging Face TRL), 60 steps
- Adapter: LoRA,
r=16 - Project: Enterprise Email Triage Simulator
- Live demo (Gemini-backed UI): https://huggingface.co/spaces/Proteinrequired/enterprise-email-triage-v2
Results — held-out tool-selection accuracy
Evaluated on a 21-email held-out test set (canonical 100-email dataset, intent-stratified 80/20 split, seed 42; BC trained only on the 79 train emails). Metric = did the policy pick the reward system's optimal tool for each email.
| Policy | Held-out accuracy (N=21) |
|---|---|
| Random choice | 33.0% |
Always route_to_human |
47.6% |
| Rule-based heuristic | 76.2% (16/21) |
| This adapter | 71.4% (15/21) |
The adapter is perfect on every route_to_human intent but over-escalates routine
(auto_reply) and ambiguous (ask_for_clarification) mail — a known Behavioral-Cloning artifact
(trained on reward-positive rollouts skewed toward escalation). It lands within one example of a
hand-tuned rule baseline and far above the random / always-escalate floors. Reproduce with
run_trained_eval.py.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = "unsloth/Llama-3.2-3B-Instruct"
adapter = "Proteinrequired/email-triage-lora"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)
The model expects the system/user prompt format defined in
run_trained_eval.py
and responds with a JSON object: {"tool": "<route_to_human|auto_reply|ask_for_clarification>"}.
Limitations
- Trained on synthetic corporate emails; not validated on real inboxes or other domains.
- 3B model — needs a GPU for low-latency inference. The live demo uses Gemini via API for CPU-only hosting; this adapter is the project's open-weights research artifact.
- Optimizes the project's reward policy; not a general-purpose safety/abuse classifier.