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

Name: Echo-SmolTools-114M-Intent-CLF-Gen
Availability: InStock

zero-shot-classification

echo-dsrn

custom_code

ethicalabs/Echo-DSRN-114M-v0.1.2

finetune

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

[!WARNING] 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.

python

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}")