Instructions to use dusersad12/NexusLM-Checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dusersad12/NexusLM-Checkpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dusersad12/NexusLM-Checkpoint")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dusersad12/NexusLM-Checkpoint") model = AutoModel.from_pretrained("dusersad12/NexusLM-Checkpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload best checkpoint (step_720, eval_accuracy 0.677) with finalized README and figures
1262f81 verified Download pytorch_model.bin from dusersad12/NexusLM-Checkpoint: direct link, hf CLI and curl.
- Browser
- Download file 23 Bytes
-
https://huggingface.co/dusersad12/NexusLM-Checkpoint/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dusersad12/NexusLM-Checkpoint/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dusersad12/NexusLM-Checkpoint/resolve/main/pytorch_model.bin
23 Bytes
- Xet hash:
- 1137d44a174774e1b8e75571b92adb6ecbdc540df1365bf00f762c9768296c16
- Size of remote file:
- 23 Bytes
- SHA256:
- 965362299a238de576a92dfdd3e32aea7a2bacc94b2c41541c8c9258b923f587
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