SOLAR-10.7B-slerp
SOLAR-10.7B-slerp is a merge of the following models using mergekit:
Github
https://github.com/sunjin7725/SOLAR-10.7b-slerp
Benchmark
Open-Ko-LLM-Leaderboard
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
|---|---|---|---|---|---|
| 56.93 | 53.58 | 62.03 | 53.31 | 57.16 | 58.56 |
How to use
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = 'SJ-Donald/SOLAR-10.7B-slerp'
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
🧩 Configuration
slices:
- sources:
- model: LDCC/LDCC-SOLAR-10.7B
layer_range: [0, 48]
- model: upstage/SOLAR-10.7B-Instruct-v1.0
layer_range: [0, 48]
merge_method: slerp
base_model: upstage/SOLAR-10.7B-Instruct-v1.0
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
tokenizer_source: union
dtype: float16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 72.58 |
| AI2 Reasoning Challenge (25-Shot) | 68.17 |
| HellaSwag (10-Shot) | 86.91 |
| MMLU (5-Shot) | 66.73 |
| TruthfulQA (0-shot) | 67.42 |
| Winogrande (5-shot) | 84.06 |
| GSM8k (5-shot) | 62.17 |