Text Classification
Transformers
Safetensors
English
qwen2
text-generation
LLM
classification
instruction-tuned
multi-label
qwen
text-embeddings-inference
Instructions to use BenchHub/BenchHub-Cat-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BenchHub/BenchHub-Cat-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BenchHub/BenchHub-Cat-7b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BenchHub/BenchHub-Cat-7b") model = AutoModelForCausalLM.from_pretrained("BenchHub/BenchHub-Cat-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from BenchHub/BenchHub-Cat-7b: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/BenchHub/BenchHub-Cat-7b/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://BenchHub/BenchHub-Cat-7b@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/BenchHub/BenchHub-Cat-7b/resolve/refs%2Fpr%2F1/tokenizer.json
11.4 MB
- Xet hash:
- a7030cf2e58dead38199a68a8cd6f6f1a609a6072d7fb38ba5f85b3bb7e21557
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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