README.md

metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - flammenai/flammen15-gutenberg-DPO-v1-7B
  - Eric111/CatunaLaserPi
base_model:
  - flammenai/flammen15-gutenberg-DPO-v1-7B
  - Eric111/CatunaLaserPi
model-index:
  - name: VisFlamCat
    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: 43.66
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=Stark2008/VisFlamCat
          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: 32.88
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=Stark2008/VisFlamCat
          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: 6.57
            name: exact match
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=Stark2008/VisFlamCat
          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: 5.37
            name: acc_norm
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=Stark2008/VisFlamCat
          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: 14.68
            name: acc_norm
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=Stark2008/VisFlamCat
          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: 23.82
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=Stark2008/VisFlamCat
          name: Open LLM Leaderboard

VisFlamCat

VisFlamCat is a merge of the following models using LazyMergekit:

• flammenai/flammen15-gutenberg-DPO-v1-7B
• Eric111/CatunaLaserPi

🧩 Configuration

models:
  - model: Nitral-AI/Visual-LaylelemonMaidRP-7B
    #no parameters necessary for base model
  - model: flammenai/flammen15-gutenberg-DPO-v1-7B
    parameters:
      density: 0.5
      weight: 0.5
  - model: Eric111/CatunaLaserPi
    parameters:
      density: 0.5
      weight: 0.5

merge_method: ties
base_model: Nitral-AI/Visual-LaylelemonMaidRP-7B
parameters:
  normalize: false
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Stark2008/VisFlamCat"
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.	21.16
IFEval (0-Shot)	43.66
BBH (3-Shot)	32.88
MATH Lvl 5 (4-Shot)	6.57
GPQA (0-shot)	5.37
MuSR (0-shot)	14.68
MMLU-PRO (5-shot)	23.82