Instructions to use 1024m/WASSA24-Task1-3A-LLAMA3-8B-V002-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 1024m/WASSA24-Task1-3A-LLAMA3-8B-V002-lora with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("1024m/WASSA24-Task1-3A-LLAMA3-8B-V002-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

Unsloth Studio

How to use 1024m/WASSA24-Task1-3A-LLAMA3-8B-V002-lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for 1024m/WASSA24-Task1-3A-LLAMA3-8B-V002-lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for 1024m/WASSA24-Task1-3A-LLAMA3-8B-V002-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 1024m/WASSA24-Task1-3A-LLAMA3-8B-V002-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="1024m/WASSA24-Task1-3A-LLAMA3-8B-V002-lora",
    max_seq_length=2048,
)

WASSA24-Task1-3A-LLAMA3-8B-V002-lora / tokenizer.json

Commit History

Trained with Unsloth

b1a8d93
verified

1024m commited on Jun 11, 2024

Commit History

Trained with Unsloth b1a8d93 verified

Trained with Unsloth

b1a8d93
verified