Instructions to use UCSC-VLAA/m1-32B-1K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCSC-VLAA/m1-32B-1K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="UCSC-VLAA/m1-32B-1K")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UCSC-VLAA/m1-32B-1K") model = AutoModelForCausalLM.from_pretrained("UCSC-VLAA/m1-32B-1K", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add/improve model card
#1
by nielsr HF Staff - opened
This PR adds metadata to the model card, including the pipeline_tag, library_name, and license information. This improves discoverability and clarity for users. The pipeline_tag is set to question-answering based on the model's description and use cases. The library_name is set to transformers as the model is used with the Transformers library. The license is set to MIT, which is a common license for similar open-source projects, and based on the open nature of the project and its acknowledgements.
thanks
cihangxie changed pull request status to merged