---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
pipeline_tag: text-generation
---
Description: Translation of video game meaning representations to natural language\
Original dataset: https://huggingface.co/datasets/GEM/viggo \
---\
Try querying this adapter for free in Lora Land at https://predibase.com/lora-land! \
The adapter_category is Structured-to-Text and the name is Structured-to-Text (viggo)\
---\
Sample input: Here are two examples of meaning representations being translated into plain English:\n\nExample representation:  "request(release_year[2014], specifier[terrible])"\nExample output: "Were there even any terrible games in 2014?"\n\nExample representation: "give_opinion(name[Little Nightmares], rating[good], genres[adventure, platformer, puzzle], player_perspective[side view])"\nExample output: "Adventure games that combine platforming and puzzles can be frustrating to play, but the side view perspective is perfect for them. That's why I enjoyed playing Little Nightmares."\n\nUsing the previous examples as guidelines, please translate the following representation into plain English:\nRepresentation: suggest(name[Little Big Adventure], player_perspective[third person], platforms[PC])\nOutput:\
---\
Sample output: Do you like third person PC games like Little Big Adventure?\
---\
Try using this adapter yourself!
```
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "mistralai/Mistral-7B-v0.1"
peft_model_id = "predibase/viggo"

model = AutoModelForCausalLM.from_pretrained(model_id)
model.load_adapter(peft_model_id)
```