metadata
tags:
- merge
- mergekit
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- beowolx/CodeNinja-1.0-OpenChat-7B
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- beowolx/CodeNinja-1.0-OpenChat-7B
license: apache-2.0
Hugo-7B-slerp
Hugo-7B-slerp is a successful merge of the following models using mergekit:
🧩 Configuration
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: beowolx/CodeNinja-1.0-OpenChat-7B
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
dtype: bfloat16
📈 Performance
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|---|
| paulilioaica/Hugo-7B-slerp | 67.07 | 64.51 | 84.77 | 62.54 | 57.13 | 80.03 | 53.45 |
| mistralai/Mistral-7B-Instruct-v0.2 | 65.71 | 63.14 | 84.88 | 60.78 | 68.26 | 77.19 | 40.03 |
| beowolx/CodeNinja-1.0-OpenChat-7B | 67.4 | 63.48 | 83.65 | 63.77 | 47.16 | 79.79 | 66.57 |
With bold one can see the benchmarks where this merge overtakes the basemodel in performance.
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "paulilioaica/Hugo-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"conversational",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(messages, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs)