Feature Extraction
Transformers
TensorBoard
Safetensors
mistral
Generated from Trainer
4-bit precision
bitsandbytes
Instructions to use VikrantRamesh/Mistral_CN_pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VikrantRamesh/Mistral_CN_pretrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="VikrantRamesh/Mistral_CN_pretrain")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("VikrantRamesh/Mistral_CN_pretrain") model = AutoModelForMultimodalLM.from_pretrained("VikrantRamesh/Mistral_CN_pretrain") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bde05505e39489def797bdc4006ee4b6a19f2b679fe97ae511d968c424d41786
- Size of remote file:
- 1.22 GB
- SHA256:
- b43f069ef7dca615afb4d6ba868874781547505a315424cc771f87ccd77f4ad1
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