Instructions to use taka-too/llm-jp-3-13b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taka-too/llm-jp-3-13b-it with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("taka-too/llm-jp-3-13b-it", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Uploaded model
- Developed by: taka-too
- License: CC-BY-NC-SA-4.0
- Finetuned from model : llm-jp/llm-jp-3-13b
- Training Dataset: Ichikara Instruction (LLM-jp)
This LLaMA-based model has been fine-tuned for enhanced instruction-following capabilities using the Ichikara Instruction dataset provided by LLM-jp, which was trained 2x faster with Unsloth and Huggingface's TRL library.
関根聡, 安藤まや, 後藤美知子, 鈴木久美, 河原大輔, 井之上直也, 乾健太郎. ichikara-instruction: LLMのための日本語インストラクションデータの構築. 言語処理学会第30回年次大会(2024)
How to Use the Model
You can load the model via the Hugging Face transformers library:
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("taka-too/llm-jp-3-13b-it")
model = AutoModelForCausalLM.from_pretrained("taka-too/llm-jp-3-13b-it")
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Base model
llm-jp/llm-jp-3-13b