Total runs:
2.0K
Run Growth:
695
Growth Rate:
35.42%
Sponsored by
APIMart
ethicalabs / Echo-DSRN-v0.1.3-Intent-CLF
Model's Last Updated: August 23 2026
text-classification
Introduction of Echo-DSRN-v0.1.3-Intent-CLF
Model Details of Echo-DSRN-v0.1.3-Intent-CLF
Model Card for ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF
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).
| Language | Accuracy | Progress |
|---|---|---|
en-US
|
81.14% |
4949/6099
|
id-ID
|
76.90% |
4690/6099
|
it-IT
|
76.57% |
4670/6099
|
da-DK
|
75.60% |
4611/6099
|
es-ES
|
75.50% |
4605/6099
|
fr-FR
|
74.95% |
4571/6099
|
pt-PT
|
73.82% |
4502/6099
|
zh-CN
|
73.57% |
4487/6099
|
lv-LV
|
73.47% |
4481/6099
|
ca-ES
|
73.44% |
2986/4066
|
nb-NO
|
73.18% |
4463/6099
|
ms-MY
|
73.13% |
4460/6099
|
af-ZA
|
73.04% |
4455/6099
|
nl-NL
|
72.86% |
4444/6099
|
tl-PH
|
72.77% |
4438/6099
|
sv-SE
|
72.16% |
4401/6099
|
ja-JP
|
72.06% |
4395/6099
|
de-DE
|
72.03% |
4393/6099
|
jv-ID
|
71.86% |
4383/6099
|
pl-PL
|
71.65% |
4370/6099
|
zh-TW
|
71.29% |
4348/6099
|
is-IS
|
70.63% |
4308/6099
|
ko-KR
|
70.60% |
4306/6099
|
az-AZ
|
69.86% |
4261/6099
|
mn-MN
|
69.77% |
4255/6099
|
ro-RO
|
69.42% |
4234/6099
|
sq-AL
|
68.81% |
4197/6099
|
cy-GB
|
68.49% |
4177/6099
|
fi-FI
|
68.21% |
4160/6099
|
fa-IR
|
67.83% |
4137/6099
|
hu-HU
|
67.52% |
4118/6099
|
vi-VN
|
67.40% |
4111/6099
|
tr-TR
|
67.14% |
4095/6099
|
sl-SL
|
66.24% |
4040/6099
|
sw-KE
|
65.73% |
4009/6099
|
el-GR
|
65.54% |
3997/6099
|
hy-AM
|
65.37% |
3987/6099
|
ru-RU
|
64.37% |
3926/6099
|
hi-IN
|
63.27% |
3859/6099
|
ml-IN
|
63.27% |
3859/6099
|
ur-PK
|
61.93% |
3777/6099
|
he-IL
|
61.80% |
3769/6099
|
ar-SA
|
60.52% |
3691/6099
|
th-TH
|
60.39% |
3683/6099
|
bn-BD
|
59.94% |
3656/6099
|
ta-IN
|
59.42% |
3624/6099
|
my-MM
|
57.70% |
3519/6099
|
ka-GE
|
57.26% |
3492/6099
|
kn-IN
|
56.85% |
3467/6099
|
am-ET
|
53.68% |
3274/6099
|
te-IN
|
51.53% |
3143/6099
|
km-KH
|
51.52% |
3142/6099
|
| -------- | -------- | -------- |
| OVERALL | 67.71% |
213375/315115
|
⚡ 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
| Concurrency | Throughput (req/s) | p50 Latency | p95 Latency | Mean Latency | Errors |
|---|---|---|---|---|---|
| 1 | 80.34 r/s | 12 ms | 15 ms | 12 ms | 0 |
| 2 | 96.39 r/s | 20 ms | 25 ms | 20 ms | 0 |
| 4 | 182.24 r/s | 20 ms | 29 ms | 21 ms | 0 |
| 8 | 356.53 r/s | 20 ms | 28 ms | 21 ms | 0 |
| 16 | 612.88 r/s | 21 ms | 37 ms | 23 ms | 0 |
| 32 | 748.84 r/s | 37 ms | 50 ms | 37 ms | 0 |
| 64 | 1318.12 r/s | 41 ms | 52 ms | 41 ms | 0 |
| 128 | 1502.82 r/s | 65 ms | 92 ms | 65 ms | 0 |
| 256 | 1553.11 r/s | 78 ms | 113 ms | 78 ms | 0 |
| 512 | 1551.50 r/s | 81 ms | 128 ms | 84 ms | 0 |
| 1024 | 1555.05 r/s | 101 ms | 164 ms | 104 ms | 0 |
| 2048 | 1453.98 r/s | 135 ms | 244 ms | 142 ms | 0 |
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}]
Runs of ethicalabs Echo-DSRN-v0.1.3-Intent-CLF on huggingface.co
36
Total runs
-3
24-hour runs
-1
3-day runs
-4
7-day runs
-249
30-day runs
More Information About Echo-DSRN-v0.1.3-Intent-CLF huggingface.co Model
Url of Echo-DSRN-v0.1.3-Intent-CLF
Echo-DSRN-v0.1.3-Intent-CLF huggingface.co Url
Provider of Echo-DSRN-v0.1.3-Intent-CLF huggingface.co
Other API from ethicalabs
Updated:August 23 2026
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Growth Rate:
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Growth Rate:
-867.71%
Total runs:
134
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65
Growth Rate:
48.51%
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35.71%
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25
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64.10%
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Growth Rate:
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44.44%
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65.71%
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51.85%
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44.44%
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Growth Rate:
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70.83%
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18
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78.26%
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20
Growth Rate:
86.96%
Total runs:
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15
Growth Rate:
65.22%
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21
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Total runs:
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12
Growth Rate:
60.00%
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13
Growth Rate:
68.42%
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3
Growth Rate:
15.79%
Total runs:
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11
Growth Rate:
57.89%
Total runs:
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-2
Growth Rate:
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Total runs:
18
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13
Growth Rate:
76.47%
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11
Growth Rate:
61.11%
Total runs:
18
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13
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72.22%
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17
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8
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47.06%
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47.06%
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11
Growth Rate:
64.71%
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23.53%
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5.88%
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