Instructions to use ThomasTheMaker/Llama3.2-1B-Llamafactory-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ThomasTheMaker/Llama3.2-1B-Llamafactory-Instruct with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B") model = PeftModel.from_pretrained(base_model, "ThomasTheMaker/Llama3.2-1B-Llamafactory-Instruct") - Notebooks
- Google Colab
- Kaggle
sft
This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the identity and the alpaca_en_demo datasets.
Model description
This is a Llama3.2-1B SFT fine-tuned using Llama-factory, q4 quantization, using 1091 data points (identity and alpaca_en_demo)
Intended uses & limitations
The intended use of this model is to find out the time taken to fine-tune LLM on different GPUs.
Result
GTX 1050 TI: 1h35m
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Model tree for ThomasTheMaker/Llama3.2-1B-Llamafactory-Instruct
Base model
meta-llama/Llama-3.2-1B