Instructions to use dice-research/lola_instructions_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dice-research/lola_instructions_lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("dice-research/lola_v1") model = PeftModel.from_pretrained(base_model, "dice-research/lola_instructions_lora") - Notebooks
- Google Colab
- Kaggle
Adapter Details
This LoRA is for dice-research/lola_v1 model. It enables the LOLA model to respond to multilingual instructions given in the alpaca (no input) format.
Example format:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Give tips on staying healthy.
### Response:
The adapter is trained on CohereLabs/aya_dataset for 2 epochs.
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Model tree for dice-research/lola_instructions_lora
Base model
dice-research/lola_v1
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("dice-research/lola_v1") model = PeftModel.from_pretrained(base_model, "dice-research/lola_instructions_lora")