Instructions to use mllm-dev/gpt2_f_experiment_1_1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mllm-dev/gpt2_f_experiment_1_1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mllm-dev/gpt2_f_experiment_1_1000")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_f_experiment_1_1000") model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_f_experiment_1_1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from mllm-dev/gpt2_f_experiment_1_1000: direct link, hf CLI and curl.
- Browser
- Download file 996 MB
-
https://huggingface.co/mllm-dev/gpt2_f_experiment_1_1000/resolve/main/optimizer.pt
- Command line
-
hf download hf://mllm-dev/gpt2_f_experiment_1_1000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/mllm-dev/gpt2_f_experiment_1_1000/resolve/main/optimizer.pt
996 MB
- Xet hash:
- 8b2d1412176513053a2540cb4076caa293cbb49236e221f94938370a5ffc222a
- Size of remote file:
- 996 MB
- SHA256:
- feb14a29780faba156fe0eb248d97ce7ee04f3da64177ea01a849f150abbc3c8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.