README.md

---
language:
- en
- ko
datasets:
- kyujinpy/KOR-OpenOrca-Platypus-v3
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
---

# **SOLAR-tail-10.7B-instruct-v1.0**  

## Model Details

**Model Developers** Kyujin Han (kyujinpy)

**Method**  
Instruction-tuning with [PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0](https://huggingface.co./PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0).  

**Datasets**  
datasets: [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co./datasets/kyujinpy/KOR-OpenOrca-Platypus-v3).  

**Hyperparameters**  
```python
python finetune.py \
    --base_model PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 \
    --data-path  kyujinpy/KOR-OpenOrca-Platypus-v3 \
    --output_dir ./SOLAR-tail-10.7B-instruct \
    --batch_size 64 \
    --micro_batch_size 1 \
    --num_epochs 1 \
    --learning_rate 3e-5 \
    --cutoff_len 4096 \
    --val_set_size 0 \
    --lora_r 16 \
    --lora_alpha 16 \
    --lora_dropout 0.05 \
    --lora_target_modules '[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 user_prompt \
    --lr_scheduler 'cosine' \
```
> Platypus repo.

# **Model Benchmark**  

## Open leaderboard
- Follow up as [link](https://huggingface.co./spaces/upstage/open-ko-llm-leaderboard).   

| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Ko-CommonGenV2 |
| --- | --- | --- | --- | --- | --- | --- | 
| **PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0** | 51.70 | 46.93 | 58.19 | 53.15 | 46.52 | 53.72 |
| PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 | 48.32 | 45.73 | 56.97 | 38.77 | 38.75 | 61.16 |
| jjourney1125/M-SOLAR-10.7B-v1.0 | 55.15 | 49.57 | 60.12 | 54.60 | 49.23 | 62.22 |

   
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```

---