Instructions to use jdchang/bt-model-lr-7e-06-step-954 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jdchang/bt-model-lr-7e-06-step-954 with Transformers:
# Load model directly from transformers import AutoTokenizer, Qwen2ForQSharp tokenizer = AutoTokenizer.from_pretrained("jdchang/bt-model-lr-7e-06-step-954") model = Qwen2ForQSharp.from_pretrained("jdchang/bt-model-lr-7e-06-step-954") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 855d070e462d2043d87007e74869af2ac5386381e70177818c127c5755ee6a0e
- Size of remote file:
- 3.09 GB
- SHA256:
- f5e80de54669c8032f0e3d9762bedab860d3e654bf0c7360b5fd77928321d8c4
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