SOLAR-tail-10.7B-Merge-v1.0
Model Details
Model Developers Kyujin Han (kyujinpy)
Method
Using Mergekit.
Merge config
slices:
- sources:
- model: upstage/SOLAR-10.7B-v1.0
layer_range: [0, 48]
- model: Yhyu13/LMCocktail-10.7B-v1
layer_range: [0, 48]
merge_method: slerp
base_model: upstage/SOLAR-10.7B-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 # fallback for rest of tensors
tokenizer_source: union
dtype: float16
Model Benchmark
Open Ko leaderboard
- Follow up as Ko-link.
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Ko-CommonGenV2 |
|---|---|---|---|---|---|---|
| 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 |
- Follow up as En-link.
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | | --- | --- | --- | --- | --- | --- | --- | --- | | PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 | 71.68 | 66.13 | 86.54 | 66.52 | 60.57 | 84.77 | 65.58 | | kyujinpy/Sakura-SOLAR-Instruct | 74.40 | 70.99 | 88.42 | 66.33 | 71.79 | 83.66 | 65.20 |
lm-evaluation-harness
gpt2 (pretrained=PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0), limit: None, provide_description: False, num_fewshot: 0, batch_size: None
| Task |Version| Metric |Value | |Stderr|
|----------------|------:|--------|-----:|---|-----:|
|kobest_boolq | 0|acc |0.5021|± |0.0133|
| | |macro_f1|0.3343|± |0.0059|
|kobest_copa | 0|acc |0.6220|± |0.0153|
| | |macro_f1|0.6217|± |0.0154|
|kobest_hellaswag| 0|acc |0.4380|± |0.0222|
| | |acc_norm|0.5380|± |0.0223|
| | |macro_f1|0.4366|± |0.0222|
|kobest_sentineg | 0|acc |0.4962|± |0.0251|
| | |macro_f1|0.3316|± |0.0113|
Implementation Code
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 71.68 |
| AI2 Reasoning Challenge (25-Shot) | 66.13 |
| HellaSwag (10-Shot) | 86.54 |
| MMLU (5-Shot) | 66.52 |
| TruthfulQA (0-shot) | 60.57 |
| Winogrande (5-shot) | 84.77 |
| GSM8k (5-shot) | 65.58 |