Instructions to use ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("ethicalabs/Echo-DSRN-114M-v0.1.2")
model = PeftModel.from_pretrained(base_model, "ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT")

Transformers

How to use ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT

SGLang

How to use ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT with Docker Model Runner:
```
docker model run hf.co/ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF-PEFT
```

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

The merged intent classification model is now available at ethicalabs/Echo-DSRN-v0.1.3-Intent-CLF, featuring a fully integrated and fine-tuned sequence classification head.

Evaluation

Language	Accuracy	Progress
`en-US`	80.47%	`4908/6099`
`id-ID`	74.78%	`4561/6099`
`it-IT`	74.78%	`4561/6099`
`da-DK`	74.52%	`4545/6099`
`es-ES`	74.42%	`4539/6099`
`fr-FR`	73.68%	`4494/6099`
`pt-PT`	73.67%	`4493/6099`
`af-ZA`	73.57%	`4487/6099`
`nb-NO`	72.39%	`4415/6099`
`nl-NL`	72.16%	`4401/6099`
`zh-CN`	71.31%	`4349/6099`
`ca-ES`	71.27%	`2898/4066`
`ms-MY`	71.27%	`4347/6099`
`de-DE`	71.06%	`4334/6099`
`sv-SE`	70.95%	`4327/6099`
`jv-ID`	69.93%	`4265/6099`
`ja-JP`	69.86%	`4261/6099`
`lv-LV`	69.86%	`4261/6099`
`pl-PL`	69.67%	`4249/6099`
`tl-PH`	69.59%	`4244/6099`
`zh-TW`	68.99%	`4208/6099`
`is-IS`	68.88%	`4201/6099`
`ro-RO`	68.08%	`4152/6099`
`ko-KR`	67.50%	`4117/6099`
`mn-MN`	66.68%	`4067/6099`
`az-AZ`	66.63%	`4064/6099`
`sq-AL`	65.93%	`4021/6099`
`fi-FI`	65.70%	`4007/6099`
`cy-GB`	65.57%	`3999/6099`
`tr-TR`	64.98%	`3963/6099`
`sl-SL`	64.16%	`3913/6099`
`hu-HU`	63.98%	`3902/6099`
`ru-RU`	62.45%	`3809/6099`
`hy-AM`	59.57%	`3633/6099`
`el-GR`	56.94%	`3473/6099`
`ka-GE`	46.78%	`2853/6099`
--------	--------	--------
OVERALL	68.64%	`149321/217531`

⚡ Execution Statistics

Evaluation Time: 367.42 seconds (6.12 minutes)
Total Samples Evaluated: 217531
Inference Throughput: 592.05 samples/sec
Batch Size: 256
Computation Device: CUDA (AMD Radeon AI PRO R9700)

💾 Memory & Resource Metrics

Peak VRAM Allocated: 7364.24 MB
VRAM Profile: 331.54 MB (first batch) ➔ 331.65 MB (final batch)
VRAM Footprint Stability: 99.9675% stable (0.00% KV cache growth)

🚀 Production Deployment Estimates

Single-GPU Concurrency: ~592.1 concurrent requests/second at peak saturation
Estimated Class Latency (per request): ~1.69 ms

Framework versions

PEFT 0.18.0

Downloads last month: 84

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

Base model

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

Finetuned

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

Adapter

(4)

this model

Merges

1 model

ethicalabs
/

Echo-DSRN-v0.1.3-Intent-CLF-PEFT