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:
- 6871658b61f73ee36a1623e2e148b704a2a4728572e4dda8daa67a4dab823d2e
- Size of remote file:
- 9.23 kB
- SHA256:
- 5e6cc3ef4600fbb308d7d0e48ad4c92e894e2603a51b0f3a1120c7c0d7a5afa2
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