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 checkpoint-144/training_args.bin from BenchHub/BenchHub-Cat-7b: direct link, hf CLI and curl.
- Browser
- Download file 11.1 kB
-
https://huggingface.co/BenchHub/BenchHub-Cat-7b/resolve/main/checkpoint-144/training_args.bin
- Command line
-
hf download hf://BenchHub/BenchHub-Cat-7b/checkpoint-144/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BenchHub/BenchHub-Cat-7b/resolve/main/checkpoint-144/training_args.bin
11.1 kB
- Xet hash:
- 1bfae89123df91d10f97e6ed66e5bd84594e64cff94467ba94feefb321eb1475
- Size of remote file:
- 11.1 kB
- SHA256:
- e82e8e0eb75d0075bcef9a41a7cd85f558ac40dc6e4c6db8fc599cd024b6b9cf
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