SJ-Donald/SJ-SOLAR-10.7b-DPO
SJ-Donald/SJ-SOLAR-10.7b-DPO is fine-tuned using DPO method.
Environment
Using Google CoLab A100
Base model
Datasets
Benchmark
Open-LLM-Leaderboard(https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|
| 72.67 | 68.26 | 86.95 | 66.73 | 67.74 | 84.21 | 62.03 |
open-ko-llm-leaderboard(https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
|---|---|---|---|---|---|
| 56.93 | 53.67 | 61.99 | 53.36 | 57.2 | 58.44 |
How to use
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = 'SJ-Donald/SJ-SOLAR-10.7b-DPO'
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
Chat Template
template = """### System:
{{system_content}}
### User:
{{question}}
### Assistant:
"""
GGUF Version
You can use gguf model file here! -> SJ-Donald/SJ-SOLAR-10.7b-DPO-GGUF
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 72.67 |
| AI2 Reasoning Challenge (25-Shot) | 68.26 |
| HellaSwag (10-Shot) | 86.95 |
| MMLU (5-Shot) | 66.73 |
| TruthfulQA (0-shot) | 67.74 |
| Winogrande (5-shot) | 84.21 |
| GSM8k (5-shot) | 62.09 |