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
- Xet hash:
- 3de78f11d89279e162b47f5bc510c5010dcd768f77b2bfebfe47a14b949a7db4
- Size of remote file:
- 5.88 kB
- SHA256:
- 57968985d878f5196883f699a1751b4408dbad7e4a38a61484c69971da51118e
路
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