mpasila/Finnish-Alpaca-Small
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How to use mpasila/Finnish-Alpaca-Small-LoRA-7B with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mpasila/Finnish-Alpaca-Small-LoRA-7B", dtype="auto")How to use mpasila/Finnish-Alpaca-Small-LoRA-7B 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 mpasila/Finnish-Alpaca-Small-LoRA-7B 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 mpasila/Finnish-Alpaca-Small-LoRA-7B to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mpasila/Finnish-Alpaca-Small-LoRA-7B to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="mpasila/Finnish-Alpaca-Small-LoRA-7B",
max_seq_length=2048,
)LoRA trained in 4-bit with 2k context using LumiOpen/Viking-7B as the base model for 1 epoch.
Dataset used is mpasila/Finnish-Alpaca-Small.
It uses Alpaca format but with a translated instruction at the start:
{
"instruction,output": "Alla on ohje, jossa kuvataan tehtävä. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Response:\n%output%",
"instruction,input,output": "Alla on ohje, jossa kuvataan tehtävä ja joka on yhdistetty kontekstia lisäävään syötteeseen. Kirjoita vastaus, joka täyttää pyynnön asianmukaisesti.\n\n### Instruction:\n%instruction%\n\n### Input:\n%input%\n\n### Response:\n%output%"
}
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
Base model
LumiOpen/Viking-7B