Token Classification
Transformers
PyTorch
Safetensors
qwen2
Generated from Trainer
axolotl
trl
prm
text-generation-inference
Instructions to use smohammadi/Qwen2.5-3B-MathShepherd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smohammadi/Qwen2.5-3B-MathShepherd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="smohammadi/Qwen2.5-3B-MathShepherd")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("smohammadi/Qwen2.5-3B-MathShepherd") model = AutoModelForTokenClassification.from_pretrained("smohammadi/Qwen2.5-3B-MathShepherd") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e4982c2ab2fa4a8d62e1679d143b7eba5ad3a2e35b569d66a105824628059e5
- Size of remote file:
- 1.21 GB
- SHA256:
- 81d8930d1acb65e82ff629d0566ea208b7e9d0db798f74e41d4e07b368919433
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.