Model Card for ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF

[!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 98 million parameters sequence classification model based on the Echo-DSRN architecture, trained on a single AMD GPU using ROCm 7.2.

It was merged from the base model ethicalabs/Echo-DSRN-114M-v0.1.2 and the PEFT adapter ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT.

Model Details

• Architecture: EchoForClassification
• Base model: ethicalabs/Echo-DSRN-114M-v0.1.2
• Adapter: ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT
• Labels: 60 Amazon MASSIVE intents (51 languages)
• Dtype: bfloat16

Evaluation

Overall accuracy on Amazon MASSIVE's 51 languages (validation set) is 67.71% (213375/315115 exact matches).

⚡ Execution Statistics

• Evaluation Time: 250.26 seconds (4.17 minutes)
• Total Samples Evaluated: 315115
• Inference Throughput: 1259.15 samples/sec
• Batch Size: 256
• Computation Device: CUDA (AMD Radeon AI PRO R9700)

💾 Memory & Resource Metrics

• Peak VRAM Allocated: 13332.67 MB
• VRAM Profile: 279.79 MB (first batch) ➔ 279.81 MB (final batch)
• VRAM Footprint Stability: 99.9913% stable (0.00% KV cache growth)

🚀 Production Deployment Estimates

• Single-GPU Concurrency: ~1259.2 concurrent requests/second at peak saturation
• Estimated Class Latency (per request): ~0.79 ms

Serving Performance Benchmark

Served via: vLLM (Eager Mode, bfloat16)
Evaluation Prompt: "voglio una pizza"
Date Evaluated: 2026-05-28 20:28 UTC

echo-intent  | (APIServer pid=1) INFO 05-28 20:40:06 [loggers.py:271] Engine 000: Avg prompt throughput: 1799.3 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0%

Usage

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

Example:

from transformers import pipeline

pipe = pipeline("text-classification", model="ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF", trust_remote_code=True)
pipe.predict("Can you order a pizza for me?")

Output

[{'label': 'takeaway_order', 'score': 0.880979061126709}]