Echo-SmolTools-114M-Intent-CLF-Gen

This repository contains experimental models designed strictly for academic evaluation and research purposes.

Critical Constraints:

• No Production Deployment: Experimental models must not be deployed in commercial, enterprise, or mission-critical environments under any circumstances.
• No Liability: Experimental models are provided "as-is" without warranties of any kind. The developers assume zero liability for downstream consequences, system integration failures, or regulatory non-compliance resulting from unauthorized deployment.

This is a generative sequence classification model based on the Echo-DSRN architecture. It was merged from the base model ethicalabs/Echo-DSRN-114M-v0.1.2 and the PEFT adapter ethicalabs/Echo-SmolTools-114M-Intent-PEFT.

No additional linear head is trained — the adapter's generative knowledge is used directly via constrained next-token scoring: for each candidate label the model sums the log-probability of each of its tokens, then picks the highest-scoring one.

Model Details

• Architecture: EchoForGenerativeClassification
• Base model: ethicalabs/Echo-DSRN-114M-v0.1.2
• Adapter: ethicalabs/Echo-SmolTools-114M-Intent-PEFT
• Labels: 60 Amazon MASSIVE intents (51 languages)
• Dtype: bfloat16
• Constraint Method: Next-token generative scoring

Usage

This model requires trust_remote_code=True to load the custom architecture.

import torch
from transformers import AutoTokenizer
from echo_dsrn.modeling_generative_clf import EchoForGenerativeClassification

model_id = "ethicalabs/Echo-SmolTools-114M-Intent-CLF-Gen" # or your hub path

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = EchoForGenerativeClassification.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# Single utterance
label, probs = model.classify("Enter your text here", tokenizer)
print(f"Prediction: {label}")