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
Download base_model_reference.json from mehedi4/SafeAge-Guard: direct link, hf CLI and curl.
- Browser
- Download file 389 Bytes
-
https://huggingface.co/mehedi4/SafeAge-Guard/resolve/main/base_model_reference.json
- Command line
-
hf download hf://mehedi4/SafeAge-Guard/base_model_reference.json
-
curl -L -o base_model_reference.json https://huggingface.co/mehedi4/SafeAge-Guard/resolve/main/base_model_reference.json
389 Bytes
| { | |
| "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 | |
| } | |