Instructions to use vcadillo/glm-4v-9b-4-bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vcadillo/glm-4v-9b-4-bits with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="vcadillo/glm-4v-9b-4-bits", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vcadillo/glm-4v-9b-4-bits", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from vcadillo/glm-4v-9b-4-bits: direct link, hf CLI and curl.
- Browser
- Download file 2.62 MB
-
https://huggingface.co/vcadillo/glm-4v-9b-4-bits/resolve/main/tokenizer.model
- Command line
-
hf download hf://vcadillo/glm-4v-9b-4-bits/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/vcadillo/glm-4v-9b-4-bits/resolve/main/tokenizer.model
2.62 MB
- Xet hash:
- 32b3828ee5bd176a56fb05ad692ece4f06f2212dc5faf2eb1a273afd632eae45
- Size of remote file:
- 2.62 MB
- SHA256:
- 5a493598071550244b2ee7f26118f3edec2150b9dfa967929a99052ac83fe716
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