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ethicalabs / Echo-SmolTools-114M-Intent-PEFT
Model's Last Updated: June 19 2026
text-generation
Introduction of Echo-SmolTools-114M-Intent-PEFT
Model Details of Echo-SmolTools-114M-Intent-PEFT
Model Card for ethicalabs/Echo-SmolTools-114M-Intent-PEFT
The Echo-SmolTools-114M-Intent-PEFT is a a LoRA-based adapter trained over the Echo-DSRN-114M-v0.1.2 base RNN architecture, optimized as multilingual intentionality classifier.
Gradio App now available: 🎙️ Echo Intent: Multilingual Intent Classifier
Model Usage
You can load the fine-tuned PEFT adapter over the base model and run intent classification inference as follows:
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the base model and tokenizer
base_model_name = "ethicalabs/Echo-DSRN-114M-v0.1.2"
base_model = AutoModelForCausalLM.from_pretrained(base_model_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
# Load the fine-tuned PEFT adapter (example: Round 28)
peft_model_name = "ethicalabs/Echo-SmolTools-114M-Intent-PEFT"
model = PeftModel.from_pretrained(base_model, peft_model_name, trust_remote_code=True)
# Prepare the prompt for multilingual intent classification
utt = "Che ore sono a Roma?" # "What time is it in Rome?"
messages = [
{"role": "system", "content": "You are a helpful multilingual intent classification assistant."},
{"role": "user", "content": f"Classify the intent of the following request: {utt}"}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=15, do_sample=False)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip()
print(f"User: {utt}")
print(f"Intent: {response}")
Output:
User: Che ore sono a Roma?
Intent: datetime_query
Base Model: ethicalabs/Echo-DSRN-114M-v0.1.2
🏗️ Architecture Details
| Property | Value |
|---|---|
| Model Type | echo_dsrn |
| Layers | 8 |
| Hidden Dim | 512 |
| Attention Heads | 4 |
| MLP Ratio | 8.0 |
| Vocab Size | 32011 |
| Hybrid Attention | True |
| RMSNorm | True |
📊 Parameter Breakdown
| Component | Parameters | % of Total |
|---|---|---|
| Total | 114.69M (114,687,488) | 100% |
| Embeddings | 16.39M | 14.29% |
| DSRN Blocks (Aggregate) | 81.91M | 71.42% |
| LM Head | 16.39M | 14.29% |
🧩 Internal Block Structure (Per Layer)
| Sub-Component | Parameters | Description |
|---|---|---|
| MLP (Feed-Forward) | 4.20M | Upscaled hidden layers |
| DSRN Slow State | 3.15M | Constant-time memory gates |
| GRU Fast State | 1.58M | Recurrent fast path |
| Surprise Gating | 264,192 | Dynamic focus mechanism |
| Normalization | 1,024 | LayerNorm / RMSNorm |
Benchmarks
📊 Absolute Multilingual Census
The following metrics represent a statistically absolute baseline, evaluated across the full 36,594-sample validation set using a deterministic greedy-decoding policy.
| Locale | Accuracy | Matches / Total |
|---|---|---|
| 🇺🇸 English (en-US) | 79.39% | 4,842 / 6,099 |
| 🇮🇹 Italian (it-IT) | 73.49% | 4,482 / 6,099 |
| 🇪🇸 Spanish (es-ES) | 72.50% | 4,422 / 6,099 |
| 🇫🇷 French (fr-FR) | 72.16% | 4,401 / 6,099 |
| 🇵🇹 Portuguese (pt-PT) | 71.77% | 4,377 / 6,099 |
| 🇩🇪 German (de-DE) | 65.52% | 3,996 / 6,099 |
| --- | --- | --- |
| 🌍 OVERALL | 72.47% | 26,520 / 36,594 |
Training procedure
This LoRA adapter has been fine-tuned (SFT) on a single AMD Radeon™ AI PRO R9700 (32 GB RAM) by using the Flower Framework and TRL , in a simulated federated learning scenario.
Training Metrics
INFO : aggregate_fit: received 2 results and 0 failures
INFO : Communication budget: used 16716.43 MB (+278.61 MB this round) / 200,000 MB
Loading weights: 100%|██████████| 139/139 [00:00<00:00, 3686.31it/s, Materializing param=model.final_norm.weight]
INFO : fit progress: (30, 0.0, {}, 926.4947838319931)
INFO : configure_evaluate: no clients selected, skipping evaluation
INFO :
INFO : [SUMMARY]
INFO : Run finished 30 round(s) in 926.49s
...
INFO : History (metrics, distributed, fit):
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INFO : 'mean_token_accuracy': [(1, 0.7308300926908851),
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INFO : 'train_loss': [(1, 1.1626044915243983),
INFO : (2, 0.5194999669492245),
INFO : (3, 0.40047831716015936),
INFO : (4, 0.3398165641538799),
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INFO : (30, 0.04514436764531638)]}
INFO :
Framework versions
- TRL: 1.1.0
- Transformers: 5.2.0
- Pytorch: 2.10.0+rocm7.1
- Datasets: 4.8.4
- Tokenizers: 0.22.2
- Flwr: 1.28.0
- Flwr-datasets: 0.6.0
Runs of ethicalabs Echo-SmolTools-114M-Intent-PEFT on huggingface.co
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More Information About Echo-SmolTools-114M-Intent-PEFT huggingface.co Model
Url of Echo-SmolTools-114M-Intent-PEFT
Echo-SmolTools-114M-Intent-PEFT huggingface.co Url
Provider of Echo-SmolTools-114M-Intent-PEFT huggingface.co
Other API from ethicalabs
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