PracticeLLM/KoSOLAR-Platypus-10.7B

Model Details

Model Developers Kyujin Han (kyujinpy)

Method
LoRA with quantization.

Base Model
yanolja/KoSOLAR-10.7B-v0.2

Dataset
kyujinpy/KOR-OpenOrca-Platypus-v3.

Hyperparameters

python finetune.py \
    --base_model yanolja/KoSOLAR-10.7B-v0.2 \
    --data-path  kyujinpy/KOR-OpenOrca-Platypus-v3 \
    --output_dir ./Ko-PlatypusSOLAR-10.7B \
    --batch_size 64 \
    --micro_batch_size 1 \
    --num_epochs 5 \
    --learning_rate 2e-5 \
    --cutoff_len 2048 \
    --val_set_size 0 \
    --lora_r 64 \
    --lora_alpha 64 \
    --lora_dropout 0.05 \
    --lora_target_modules '[embed_tokens, q_proj, k_proj, v_proj, o_proj, gate_proj, down_proj, up_proj, lm_head]' \
    --train_on_inputs False \
    --add_eos_token False \
    --group_by_length False \
    --prompt_template_name en_simple \
    --lr_scheduler 'cosine' \

Share all of things. It is my belief.

Model Benchmark

Open Ko-LLM leaderboard & lm-evaluation-harness(zero-shot)

• Follow up as Ko-link.

  Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Ko-CommonGenV2
  PracticeLLM/KoSOLAR-Platypus-10.7B	---	---	---	---	---	---
  LDCC/LDCC-SOLAR-10.7B	59.34	55.38	65.56	53.38	64.39	57.97
  yanolja/KoSOLAR-10.7B-v0.2	55.62	50.51	62.29	53.76	47.31	64.23
  megastudyedu/M-SOLAR-10.7B-v1.3	56.64	51.37	60.93	54.91	48.45	67.53

Implementation Code

### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "PracticeLLM/KoSOLAR-Platypus-10.7B"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)