---
license: apache-2.0
language:
- en
pipeline_tag: text-classification
base_model: convaiinnovations/laya
base_model_relation: adapter
datasets:
- bitext/Bitext-customer-support-llm-chatbot-training-dataset
tags:
- ariadne
- laya
- adapter
- custom-code
---

# Ariadne-Laya-Support

Routes customer requests to 27 support intents. This **4.2 MB specialist interface** runs on a shared frozen Laya base. Switching between compatible Ariadne specialists replaces about **1 million parameters**, while the **421 million parameter base** stays in memory. Each specialist uses its own small interface.

## Use

Install the included Python wheel from this downloaded model folder:

```bash
pip install ./ariadne_specialists-0.2.0a1-py3-none-any.whl
```

```python
import ariadne
from ariadne.specialists import Support

model = ariadne.load_specialist(Support, model=".")
result = model("Where is my refund?")
print(result.label, result.score)
```

The base downloads automatically and is cached. Pass `device="cpu"` or `device="cuda:1"` to choose a device. A list of texts returns a list of predictions. Scores have not been recalibrated for this task.

## Load from Hugging Face

After installing the included wheel, you can load this repository directly:

```python
import ariadne
from ariadne.specialists import Support

model = ariadne.load_specialist(Support, model="GoatHerder/Ariadne-Laya-Support")
```

Use the explicit `model=` argument with this preview wheel. The interface and pinned base are downloaded automatically and cached. Pass `revision="<commit hash>"` to pin a particular interface version.

## Interface

The interface is a 1,024 × 1,024 linear projection plus a 1,024-element bias: **1,049,600 trainable parameters**. It sits after the base's native embeddings and before encoder block 0. It starts as the identity; training updates only this projection. The shared base has 421,293,827 parameters. Compatible specialists share one resident base in the same Python process and on the same device.

## Results

All local results below use the same **2,166** requests and label task.

| Model | Accuracy | Macro-F1 |
|---|---:|---:|
| Base Laya | 97.14% | 97.28% |
| Ariadne Support | 99.77% | 99.73% |
| [learn-abc/magicSupport-intent-classifier](https://huggingface.co/learn-abc/magicSupport-intent-classifier) | 99.95% | 99.96% |
| TF-IDF + logistic regression | 99.22% | 99.17% |

The public fine-tune's training overlap with these cases is unknown, so this table does not establish a common unseen-test ranking. Full results and existing-task retention are in `metrics.json`.

## Training and scope

Trained on [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset), revision `430d1a89bd93bd1fa23c16f29dd53e73f0087443`. One seed (0); epoch 3 selected by validation loss. LR 1e-4; only the 1,049,600 interface parameters were trained. The base embeddings, 28 encoder blocks and decision heads stayed frozen.

English, single-topic inputs and the labels in `task.json`. This source-corpus benchmark is easy and does not establish accuracy on real customer traffic. Unsupported or ambiguous requests still receive a label. Source datasets retain their own licences.

Base revision: `55cf4c4ebb4ebe31b2550e8bdf3bd21b99753851`. Independent adaptation; no affiliation with the original Laya authors.