PEFT
Safetensors
English
lora
metadata-extraction
dcat
Knowledge-Graph
Scientific-metadata
Dataset-benchmarking
Multi-path-LinkPrediction
Chain-of-Thoughts
Instructions to use SDM-TIB/MetaMine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SDM-TIB/MetaMine with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct") model = PeftModel.from_pretrained(base_model, "SDM-TIB/MetaMine") - Notebooks
- Google Colab
- Kaggle
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Download README.md from SDM-TIB/MetaMine: direct link, hf CLI and curl.
- Browser
- Download file 267 Bytes
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https://huggingface.co/SDM-TIB/MetaMine/resolve/main/README.md
- Command line
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hf download hf://SDM-TIB/MetaMine/README.md
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curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/SDM-TIB/MetaMine/resolve/main/README.md
267 Bytes
| license: llama3.2 | |
| language: | |
| - en | |
| base_model: | |
| - meta-llama/Llama-3.2-3B-Instruct | |
| library_name: peft | |
| tags: | |
| - lora | |
| - peft | |
| - metadata-extraction | |
| - dcat | |
| - Knowledge-Graph | |
| - Scientific-metadata | |
| - Dataset-benchmarking | |
| - Multi-path-LinkPrediction | |
| - Chain-of-Thoughts | |