SOLAR-Platypus-10.7B-v2
Model Details
Model Developers Kyujin Han (kyujinpy)
Input Models input text only.
Output Models generate text only.
Model Architecture
SOLAR-Platypus-10.7B-v2 is an auto-regressive language model based on the Llama2 architecture.
Base Model
upstage/SOLAR-10.7B-v1.0
Training Dataset
garage-bAInd/Open-Platypus.
Notice
While training, I used Q-LoRA.
The lora_r values is 64.
Q-LoRA config
- LoRA_r: 64
- LoRA_alpha: 16
- LoRA_dropout: 0.05
- LoRA_target_modules: [gate_proj, up_proj, down_proj, q_proj, k_proj, v_proj]
Prompt
## Human:
## Assistant:
Model Benchmark
Open leaderboard
- Follow up as link.
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|---|
| SOLAR-Platypus-10.7B-v1 | 58.62 | 61.69 | 84.23 | 60.37 | 51.58 | 82.79 | 11.07 |
| SOLAR-Platypus-10.7B-v2 | 55.25 | 59.39 | 83.57 | 59.93 | 43.15 | 81.45 | 4.02 |
| upstage/SOLAR-10.7B-v1.0 | 66.04 | 61.95 | 84.60 | 65.48 | 45.04 | 83.66 | 55.50 |
Implementation Code
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/SOLAR-Platypus-10.7B-v2"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)