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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

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|Β±  |0.0133|
|                |       |macro_f1|0.3343|Β±  |0.0059|
|kobest_copa     |      0|acc     |0.6220|Β±  |0.0153|
|                |       |macro_f1|0.6217|Β±  |0.0154|
|kobest_hellaswag|      0|acc     |0.4380|Β±  |0.0222|
|                |       |acc_norm|0.5380|Β±  |0.0223|
|                |       |macro_f1|0.4366|Β±  |0.0222|
|kobest_sentineg |      0|acc     |0.4962|Β±  |0.0251|
|                |       |macro_f1|0.3316|Β±  |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
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