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 # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="UCSC-VLAA/m1-7B-23K")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from UCSC-VLAA/m1-7B-23K: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/UCSC-VLAA/m1-7B-23K/resolve/main/tokenizer.json
- Command line
-
hf download hf://UCSC-VLAA/m1-7B-23K/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/UCSC-VLAA/m1-7B-23K/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- a7030cf2e58dead38199a68a8cd6f6f1a609a6072d7fb38ba5f85b3bb7e21557
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
路
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