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:
- 85c7393621cc82ecf1b2dd593cf626a8bbf06e0dab406e368efff395c955eedd
- Size of remote file:
- 13.6 MB
- SHA256:
- 7a1a45e9947d86c4b6a51cde03ba021fb7e6d01bccb357364abc5ff992af690d
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