# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("toolathlonEval/HarborIntent-Compact-Release")
model = AutoModelForSequenceClassification.from_pretrained("toolathlonEval/HarborIntent-Compact-Release", device_map="auto")Quick Links
HarborIntent-Compact
HarborIntent-Compact is a small assistant-routing model designed for intent detection, slot extraction, and safe tool selection in customer-support workflows.
Release Evaluation
The release candidate was evaluated with the frozen HarborBench suite. All values are normalized to the range 0–1.
| Capability | HarborIntent-Compact |
|---|---|
| Intent Accuracy | 0.851 |
| Slot F1 | 0.829 |
| Entity Recall | 0.824 |
| Multi-turn Success | 0.801 |
| Tool-routing Accuracy | 0.846 |
| Groundedness | 0.837 |
| Safety Pass Rate | 0.910 |
| Response Conciseness | 0.792 |
Intended Use
Use this model to route English-language support requests to a constrained set of approved tools. Review outputs before using them in high-impact decisions.
Loading
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("HarborIntent-Compact-Release")
model = AutoModel.from_pretrained("HarborIntent-Compact-Release")
Limitations
The model is not designed for open-ended generation, medical advice, legal advice, or autonomous financial actions.
License
Apache-2.0.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="toolathlonEval/HarborIntent-Compact-Release")