Text Classification
PEFT
Safetensors
Transformers
Bengali
English
qwen3
text-generation
lora
safety-classification
bangla
banglish
safeage
text-embeddings-inference
Instructions to use mehedi4/SafeAge-Guard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mehedi4/SafeAge-Guard with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("Qwen/Qwen3-1.7B") model = PeftModel.from_pretrained(base_model, "mehedi4/SafeAge-Guard") - Transformers
How to use mehedi4/SafeAge-Guard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mehedi4/SafeAge-Guard")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mehedi4/SafeAge-Guard") model = AutoModelForCausalLM.from_pretrained("mehedi4/SafeAge-Guard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 389 Bytes
d292588 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"model_source": "Qwen/Qwen3-1.7B",
"model_revision": "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e",
"resolved_model_revision": "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e",
"pad_token_id": 151643,
"num_labels": 2,
"id2label": {
"0": "benign",
"1": "attack"
},
"model_architecture": "qwen3",
"precision": "float32",
"training_method": "lora",
"max_length": 512
}
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