Feature Extraction
Transformers
Safetensors
English
smb_unstructured
text-generation
World Model
Patient Representation Encoder
Feature Extraction
Joint Embedding Predictive Architecture (JEPA)
custom_code
Instructions to use anon-9421/smb-structure-llama3-8b-multi-objective with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anon-9421/smb-structure-llama3-8b-multi-objective with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="anon-9421/smb-structure-llama3-8b-multi-objective", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("anon-9421/smb-structure-llama3-8b-multi-objective", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 931a0567ebf7ab8a598bb491bb777e35170d30ed59ef292163794d21f5bc239f
- Size of remote file:
- 17.2 MB
- SHA256:
- 93790e4112b58055c8b71f4df81f2031b56d36c87689160e18d0391663d03d29
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