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
Download value_stats.pth from internlm/internlm-xcomposer2d5-7b-4bit: direct link, hf CLI and curl.
- Browser
- Download file 224 kB
-
https://huggingface.co/internlm/internlm-xcomposer2d5-7b-4bit/resolve/main/value_stats.pth
- Command line
-
hf download hf://internlm/internlm-xcomposer2d5-7b-4bit/value_stats.pth
-
curl -L -o value_stats.pth https://huggingface.co/internlm/internlm-xcomposer2d5-7b-4bit/resolve/main/value_stats.pth
224 kB
- Xet hash:
- 4eabfc0ec74410397e9a8bfdc7ae69e6402d6a2ececd7f99b89a4e893450aa92
- Size of remote file:
- 224 kB
- SHA256:
- 603d350ad8e890928b4c2ea5302bdf6c7d3a657a1c3a98ebdacea44054a0b540
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