Instructions to use VikrantRamesh/llama-pretrain-CN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VikrantRamesh/llama-pretrain-CN with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="VikrantRamesh/llama-pretrain-CN")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("VikrantRamesh/llama-pretrain-CN") model = AutoModel.from_pretrained("VikrantRamesh/llama-pretrain-CN") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5453dccfab20be646135e25eb57aa35b9f31dbe0f381500f1abfbe3b0b3fc3c5
- Size of remote file:
- 2.36 GB
- SHA256:
- 1e8cfdf01bfa477691346e37a826bc2a6d0a9f3077725ad07a4814cb10028c0e
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