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---
language:
- en
- id
- jv
- su
license: gemma
tags:
- merge
- mergekit
- autoquant
- gguf
base_model:
- GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct
- aisingapore/gemma2-9b-cpt-sea-lionv3-instruct
model-index:
- name: gemma2-9b-sahabatai-v1-instruct-BaseTIES
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 73.78
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gmonsoon/gemma2-9b-sahabatai-v1-instruct-BaseTIES
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 43.4
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gmonsoon/gemma2-9b-sahabatai-v1-instruct-BaseTIES
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 19.34
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gmonsoon/gemma2-9b-sahabatai-v1-instruct-BaseTIES
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 9.4
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gmonsoon/gemma2-9b-sahabatai-v1-instruct-BaseTIES
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 19.13
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gmonsoon/gemma2-9b-sahabatai-v1-instruct-BaseTIES
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 37.19
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=gmonsoon/gemma2-9b-sahabatai-v1-instruct-BaseTIES
name: Open LLM Leaderboard
---
# SahabatAI-Lion-9B-TIES-v1
formerly gemma2-9b-cpt-sahabatai-v1-instruct-BaseTIES (model name too long :D )
Based on some research, when a finetuned model is merged with its base model with TIES method, there is possibility the merged model will achieve better output.
**UPDATE!!! as 20 November 2024, this model is third best model (number one for Gemma2-9B based model) on HF's Open LLM Leaderboard (with Merge/MoErges hide model unchecked) for LLM model below 10B parameters.**
gmonsoon/SahabatAI-Lion-9B-TIES-v1 is a merge of the following models:
* [GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct)
* [aisingapore/gemma2-9b-cpt-sea-lionv3-instruct](https://huggingface.co/aisingapore/gemma2-9b-cpt-sea-lionv3-instruct)
DEMO Spaces: [HERE](https://huggingface.co/spaces/gmonsoon/SahabatAI-Lion-9B-TIES-v1)
## 🧩 Configuration
```yaml
models:
- model: GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct
parameters:
weight: 1
density: 1
- model: GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct
parameters:
weight: 1
density: 1
merge_method: ties
base_model: aisingapore/gemma2-9b-cpt-sea-lionv3-instruct
parameters:
density: 1
normalize: true
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "gmonsoon/SahabatAI-Lion-9B-TIES-v1"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_gmonsoon__gemma2-9b-sahabatai-v1-instruct-BaseTIES)
| Metric |Value|
|-------------------|----:|
|Avg. |33.70|
|IFEval (0-Shot) |73.78|
|BBH (3-Shot) |43.40|
|MATH Lvl 5 (4-Shot)|19.34|
|GPQA (0-shot) | 9.40|
|MuSR (0-shot) |19.13|
|MMLU-PRO (5-shot) |37.19|
|