Instructions to use UCSC-VLAA/m1-7B-23K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCSC-VLAA/m1-7B-23K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="UCSC-VLAA/m1-7B-23K")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UCSC-VLAA/m1-7B-23K") model = AutoModelForCausalLM.from_pretrained("UCSC-VLAA/m1-7B-23K") - Notebooks
- Google Colab
- Kaggle
Add model card metadata and link to code
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding the relevant
pipeline_tagso that the model can be found at https://huggingface.co/models?pipeline_tag=question-answering. - Linking the model to the paper page (https://huggingface.co/papers/2504.00869) and Github repository (https://github.com/UCSC-VLAA/m1).
- Adding
library_nameandlicense.
thanks
cihangxie changed pull request status to merged