Instructions to use cammy/glm-roberta-large-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cammy/glm-roberta-large-finetune with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("cammy/glm-roberta-large-finetune", trust_remote_code=True) model = AutoModelForSeq2SeqLM.from_pretrained("cammy/glm-roberta-large-finetune", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from cammy/glm-roberta-large-finetune: direct link, hf CLI and curl.
- Browser
- Download file 1.26 kB
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https://huggingface.co/cammy/glm-roberta-large-finetune/resolve/main/README.md
- Command line
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hf download hf://cammy/glm-roberta-large-finetune/README.md
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curl -L -o README.md https://huggingface.co/cammy/glm-roberta-large-finetune/resolve/main/README.md
1.26 kB
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: glm-roberta-large-finetune | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # glm-roberta-large-finetune | |
| This model is a fine-tuned version of [BAAI/glm-roberta-large](https://huggingface.co/BAAI/glm-roberta-large) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: nan | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 1e-05 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.0 | 1.0 | 4774 | nan | | |
| ### Framework versions | |
| - Transformers 4.24.0 | |
| - Pytorch 1.13.0+cu116 | |
| - Datasets 2.7.0 | |
| - Tokenizers 0.13.2 | |