Instructions to use dusersad12/NexusLM-BenchHub with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dusersad12/NexusLM-BenchHub with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dusersad12/NexusLM-BenchHub")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dusersad12/NexusLM-BenchHub") model = AutoModel.from_pretrained("dusersad12/NexusLM-BenchHub", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dusersad12/NexusLM-BenchHub: direct link, hf CLI and curl.
- Browser
- Download file 2.75 kB
-
https://huggingface.co/dusersad12/NexusLM-BenchHub/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dusersad12/NexusLM-BenchHub/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dusersad12/NexusLM-BenchHub/resolve/main/pytorch_model.bin
2.75 kB
- Xet hash:
- ffb1c4f8c9a3ff3fea156717e7eac54576ef6e545343bdf9c7efef3754896ed5
- Size of remote file:
- 2.75 kB
- SHA256:
- e34b1bb3bff66d36853edfc0db82e71a056a299118410571b6bfead769b174f6
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