Kukedlc_-_NeuralFusion-7b-Dare-Ties-4bits

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NeuralFusion-7b-Dare-Ties - bnb 4bits

• Model creator: https://huggingface.co/Kukedlc/
• Original model: https://huggingface.co/Kukedlc/NeuralFusion-7b-Dare-Ties/

Original model description:

license: apache-2.0 tags:

• merge
• mergekit
• lazymergekit
• Kukedlc/NeuralMaxime-7B-slerp
• Kukedlc/Fasciculus-Arcuatus-7B-slerp
• Kukedlc/NeoCortex-7B-slerp base_model:
• Kukedlc/NeuralMaxime-7B-slerp
• Kukedlc/Fasciculus-Arcuatus-7B-slerp
• Kukedlc/NeoCortex-7B-slerp model-index:
• name: NeuralFusion-7b-Dare-Ties results:
  • task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
    • type: acc_norm value: 73.21 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralFusion-7b-Dare-Ties name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
    • type: acc_norm value: 88.96 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralFusion-7b-Dare-Ties name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
    • type: acc value: 64.77 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralFusion-7b-Dare-Ties name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
    • type: mc2 value: 73.32 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralFusion-7b-Dare-Ties name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
    • type: acc value: 85.56 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralFusion-7b-Dare-Ties name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
    • type: acc value: 69.83 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralFusion-7b-Dare-Ties name: Open LLM Leaderboard

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NeuralFusion-7b-Dare-Ties

NeuralFusion-7b-Dare-Ties is a merge of the following models using LazyMergekit:

• Kukedlc/NeuralMaxime-7B-slerp
• Kukedlc/Fasciculus-Arcuatus-7B-slerp
• Kukedlc/NeoCortex-7B-slerp

🧩 Configuration

models:
  - model: mlabonne/Monarch-7B
    # no parameters necessary for base model
  - model: Kukedlc/NeuralMaxime-7B-slerp
    parameters:
      density: 0.65
      weight: 0.36
  - model: Kukedlc/Fasciculus-Arcuatus-7B-slerp
    parameters:
      density: 0.6
      weight: 0.34
  - model: Kukedlc/NeoCortex-7B-slerp
    parameters:
      density: 0.6
      weight: 0.3
merge_method: dare_ties
base_model: mlabonne/Monarch-7B
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuralFusion-7b-Dare-Ties"
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