Instructions to use mllm-dev/gpt2_m_experiment_dare_linear_1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/gpt2_m_experiment_dare_linear_1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/gpt2_m_experiment_dare_linear_1000")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_m_experiment_dare_linear_1000") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_m_experiment_dare_linear_1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from mllm-dev/gpt2_m_experiment_dare_linear_1000: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/mllm-dev/gpt2_m_experiment_dare_linear_1000/resolve/main/tokenizer.json
- Command line
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hf download hf://mllm-dev/gpt2_m_experiment_dare_linear_1000/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/mllm-dev/gpt2_m_experiment_dare_linear_1000/resolve/main/tokenizer.json
2.11 MB
File too large to display, you can check the raw version instead.