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.
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}")