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metadata
language:
  - en
datasets:
  - garage-bAInd/Open-Platypus
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0

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)