Instructions to use weqweasdas/RM-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use weqweasdas/RM-Mistral-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="weqweasdas/RM-Mistral-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("weqweasdas/RM-Mistral-7B") model = AutoModelForSequenceClassification.from_pretrained("weqweasdas/RM-Mistral-7B") - Notebooks
- Google Colab
- Kaggle
why vocab size is 32001
#3
by yechenzhi1 - opened
just out of curiosity, why do you increase the vocab size from 32000 to 32001.
because mistral does not have a padding token so we add a [PAD]. you cannot use the eos token as the pad token because in this case the multi-turn conversation does not work probably because the chat template of mistral.