Instructions to use daniel40/041e22ef-30e6-4c50-ade2-91c16e96113e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use daniel40/041e22ef-30e6-4c50-ade2-91c16e96113e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct") model = PeftModel.from_pretrained(base_model, "daniel40/041e22ef-30e6-4c50-ade2-91c16e96113e") - Notebooks
- Google Colab
- Kaggle
041e22ef-30e6-4c50-ade2-91c16e96113e
This model is a fine-tuned version of aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3051
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
- Downloads last month
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Model tree for daniel40/041e22ef-30e6-4c50-ade2-91c16e96113e
Base model
meta-llama/Meta-Llama-3-8B-Instruct Finetuned
aisingapore/Llama-SEA-LION-v2-8B Finetuned
aisingapore/Llama-SEA-LION-v2-8B-IT