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
-
https://huggingface.co/SDM-TIB/MetaMine/resolve/main/README.md
- Command line
-
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
metadata
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