Visual Question Answering
Transformers
PyTorch
internlm2
feature-extraction
custom_code
4-bit precision
awq
Instructions to use internlm/internlm-xcomposer2d5-7b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/internlm-xcomposer2d5-7b-4bit 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="internlm/internlm-xcomposer2d5-7b-4bit", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("internlm/internlm-xcomposer2d5-7b-4bit", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Ctrl+K