ArapCheruiyot/ng_elections_meta-narratives
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How to use ArapCheruiyot/disarm_ew-llama3 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="ArapCheruiyot/disarm_ew-llama3") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("ArapCheruiyot/disarm_ew-llama3")
model = AutoModelForCausalLM.from_pretrained("ArapCheruiyot/disarm_ew-llama3")How to use ArapCheruiyot/disarm_ew-llama3 with Unsloth Studio:
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 ArapCheruiyot/disarm_ew-llama3 to start chatting
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 ArapCheruiyot/disarm_ew-llama3 to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ArapCheruiyot/disarm_ew-llama3 to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="ArapCheruiyot/disarm_ew-llama3",
max_seq_length=2048,
)This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.