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:
- 8162313b87e30ea539ff852c0ae12df60b7081b072fd7c80ca9c0c9fc5a59783
- Size of remote file:
- 5 GB
- SHA256:
- 25f78faf65000fbe6e2b32b83ebad991156cdc5c9ae8c155167cab5f4bf8f8e2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.