Instructions to use openbmb/OmniLMM-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/OmniLMM-12B with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "visual-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("visual-question-answering", model="openbmb/OmniLMM-12B")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("openbmb/OmniLMM-12B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from openbmb/OmniLMM-12B: direct link, hf CLI and curl.
- Browser
- Download file 23.2 GB
-
https://huggingface.co/openbmb/OmniLMM-12B/resolve/d5746e1781d73628585495a304652ec185a67168/pytorch_model.bin
- Command line
-
hf download hf://openbmb/OmniLMM-12B@d5746e1781d73628585495a304652ec185a67168/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/openbmb/OmniLMM-12B/resolve/d5746e1781d73628585495a304652ec185a67168/pytorch_model.bin
23.2 GB
- Xet hash:
- 8bbccba567778f022693b49ce82456af384c5552f1026fe1dcc95a2a39af98c8
- Size of remote file:
- 23.2 GB
- SHA256:
- a8b8bc958406ad8de704860fc9d772e16aff0c1759936eaacbf82c5912d68654
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