Token Classification
Transformers
Safetensors
qwen2
Generated from Trainer
trl
prm
axolotl
text-generation-inference
Instructions to use jacopo-minniti/Qwen2.5-Math-7B-PUM-half_entropy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jacopo-minniti/Qwen2.5-Math-7B-PUM-half_entropy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jacopo-minniti/Qwen2.5-Math-7B-PUM-half_entropy")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jacopo-minniti/Qwen2.5-Math-7B-PUM-half_entropy") model = AutoModelForTokenClassification.from_pretrained("jacopo-minniti/Qwen2.5-Math-7B-PUM-half_entropy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0c869c934408536b05bba67a7d60134c8985ed0d35d18a45c1fecb0bb062419
- Size of remote file:
- 11.4 MB
- SHA256:
- 722897c70e27210956de02484f1d3781763a9ae2a1b4f00b6c72b8a7974c40a1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.