Question Answering
Transformers
Safetensors
Swahili
English
llama
text-generation
text-generation-inference
Instructions to use Jacaranda/UlizaLlama3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jacaranda/UlizaLlama3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Jacaranda/UlizaLlama3")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Jacaranda/UlizaLlama3") model = AutoModelForCausalLM.from_pretrained("Jacaranda/UlizaLlama3") - Notebooks
- Google Colab
- Kaggle
Add Llama 3.1 training scripts
#9
by steveowk - opened
This PR adds comprehensive scripts for Llama 3.1 model training:
- main_cpt_llama3_1.py: Continual pre-training script with LoRA configuration
- merge_and_save.py: Model merging utility for PEFT adapters
- ReadME.md: Documentation and usage instructions
These scripts enable fine-tuning of Llama 3.1 models with efficient parameter updates using LoRA (Low-Rank Adaptation) and provide utilities for merging the trained adapters back into the base model.