Instructions to use Ezekiel999/AksaraLLM-20B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ezekiel999/AksaraLLM-20B-Instruct with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ezekiel999/AksaraLLM-20B-Instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Ezekiel999/AksaraLLM-20B-Instruct: direct link, hf CLI and curl.
- Browser
- Download file 614 Bytes
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https://huggingface.co/Ezekiel999/AksaraLLM-20B-Instruct/resolve/main/README.md
- Command line
-
hf download hf://Ezekiel999/AksaraLLM-20B-Instruct/README.md
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curl -L -o README.md https://huggingface.co/Ezekiel999/AksaraLLM-20B-Instruct/resolve/main/README.md
614 Bytes
metadata
license: apache-2.0
language: id
library_name: transformers
tags:
- aksarallm
- indonesian
- from-scratch
- smoke-test
Ezekiel999/AksaraLLM-20B-Instruct (smoke-test checkpoint)
This is NOT the production 20B model. It is a randomly-initialized
tiny preset (2 layers, 64-dim, vocab 256) pushed from a Devin
scaffolding session to validate the aksaraLLMModel.save_pretrained →
HF → aksaraLLMModel.from_pretrained round-trip.
The real 20B model (42 layers, 6144-dim, vocab 131 072) must be trained
from random initialisation on a TPU v5p pod using
aksara-train/scripts/train_20b_pretrain.py.