
# **PracticeLLM/KoSOLAR-Platypus-10.7B**  

## Model Details

**Model Developers** Kyujin Han (kyujinpy)
     
**Method**    
LoRA with quantization.
  
**Base Model**  
[yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2)  

**Dataset**  
[kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/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](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard).    
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Ko-CommonGenV2 |
| --- | --- | --- | --- | --- | --- | --- | 
| PracticeLLM/KoSOLAR-Platypus-10.7B | --- | --- | --- | --- | --- | --- | 
| [LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B) | 59.34 | 55.38 | 65.56 | 53.38 | 64.39 | 57.97 |
| [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/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](https://huggingface.co/megastudyedu/M-SOLAR-10.7B-v1.3) | 56.64 | 51.37 | 60.93 | 54.91 | 48.45 | 67.53 |  

    
# Implementation Code
```python
### 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)
```  
{"language": ["ko"], "license": "cc-by-nc-sa-4.0", "library_name": "transformers", "datasets": ["kyujinpy/KOR-OpenOrca-Platypus-v3"], "pipeline_tag": "text-generation"}
transformers safetensors llama text-generation ko dataset:kyujinpy/KOR-OpenOrca-Platypus-v3 license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us