LLM Lora
Collection
16 items • Updated • 3
How to use bunnycore/Llama-3.2-3b-RRP-lora_model with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bunnycore/Llama-3.2-3b-RRP-lora_model", dtype="auto")How to use bunnycore/Llama-3.2-3b-RRP-lora_model 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 bunnycore/Llama-3.2-3b-RRP-lora_model 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 bunnycore/Llama-3.2-3b-RRP-lora_model to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bunnycore/Llama-3.2-3b-RRP-lora_model to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="bunnycore/Llama-3.2-3b-RRP-lora_model",
max_seq_length=2048,
)dataset = load_dataset("NovaSky-AI/Sky-T1_data_17k", split="train")
dataset2 = load_dataset("Nitral-AI/Discover-Intstruct-6k-Distilled-R1-70b-ShareGPT", split="train")
dataset3 = load_dataset("Nitral-Archive/RP_Alignment-ShareGPT", split="train")
dataset4 = load_dataset("alexandreteles/AlpacaToxicQA_ShareGPT", split="train")
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bunnycore/Llama-3.2-3b-RRP-lora_model", dtype="auto")