metadata

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:

DEMO Spaces: HERE

🧩 Configuration

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

!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

Detailed results can be found here

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