Instructions to use lancehar/mean_layernorm_linear_seed6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lancehar/mean_layernorm_linear_seed6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lancehar/mean_layernorm_linear_seed6", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("lancehar/mean_layernorm_linear_seed6", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lancehar/mean_layernorm_linear_seed6: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/lancehar/mean_layernorm_linear_seed6/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lancehar/mean_layernorm_linear_seed6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lancehar/mean_layernorm_linear_seed6/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- 1d796fce419358e1fde035ceab84dca829f75d0bf2fc9acf5a75aaa507df8706
- Size of remote file:
- 115 MB
- SHA256:
- f5277f93106c5f7312d2d9fe666702cc1b43a6de7f74bcfcbdfafe66eef0a8f6
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