metadata
language:
- ko
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
SOLAR-10.7B-v1.0-Instruct
Model Details
Model Developers
- myeonghoon kim
Model Architecture
- SOLAR-10.7B-v1.0-Instruct is an auto-regressive language model based on the LLaMA2 transformer architecture.
Base Model
Training Dataset
Model comparisons1
Ko-LLM leaderboard(11/23; link)
| Model | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
|---|---|---|---|---|---|---|
| [...your_model_name...] | NaN | NaN | NaN | NaN | NaN | NaN |
Model comparisons2
AI-Harness evaluation; link
| Model | Copa | Copa | HellaSwag | HellaSwag | BoolQ | BoolQ | Sentineg | Sentineg |
|---|---|---|---|---|---|---|---|---|
| 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot | |
| SOLAR-10.7B-v1.0-Instruct | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
Implementation Code
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "[...your_model_repo...]"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)