Text Classification
Transformers
Safetensors
English
qwen2
text-generation
news
alert-detection
qwen
lora
json-output
text-embeddings-inference
4-bit precision
bitsandbytes
Instructions to use beaotero05/qwen2-Alarm-json with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beaotero05/qwen2-Alarm-json with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="beaotero05/qwen2-Alarm-json")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("beaotero05/qwen2-Alarm-json") model = AutoModelForMultimodalLM.from_pretrained("beaotero05/qwen2-Alarm-json") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 967a9d3af87fcb0aac99c7cd3d496cddaeb59f3afca79d05af6ed82b4a931fb7
- Size of remote file:
- 4.46 GB
- SHA256:
- 59fc06c3f47e7ecc861b0d6fe238b7a29f9572083a8af3f12e8211962139245c
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