Kukedlc_-_NeuralMaths-Experiment-7b-gguf

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NeuralMaths-Experiment-7b - GGUF

• Model creator: https://huggingface.co/Kukedlc/
• Original model: https://huggingface.co/Kukedlc/NeuralMaths-Experiment-7b/

Original model description:

license: apache-2.0 tags:

• merge
• mergekit
• lazymergekit
• WizardLM/WizardMath-7B-V1.1
• mlabonne/NeuralDaredevil-7B
• Kukedlc/Neural4gsm8k
• Eric111/Mayo
• Kukedlc/NeuralSirKrishna-7b base_model:
• WizardLM/WizardMath-7B-V1.1
• mlabonne/NeuralDaredevil-7B
• Kukedlc/Neural4gsm8k
• Eric111/Mayo
• Kukedlc/NeuralSirKrishna-7b model-index:
• name: NeuralSirKrishna-7b results:
  • 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: 75.21 name: accuracy
    • type: acc value: 75.21 name: accuracy
  • 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: 69.71 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralMaths-Experiment-7b 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: 87.48 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralMaths-Experiment-7b 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: 65.01 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralMaths-Experiment-7b 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: 63.83 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralMaths-Experiment-7b 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: 82.48 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralMaths-Experiment-7b name: Open LLM Leaderboard

---

<div style="font-size: 42px; text-align: center;">🤖 NeuralMaths-Experiment-7b 🤖</div> <div style="font-size: 32px; text-align: center;">🔝 Number One in GSM8K LeaderBoard! 🏆</div>

NeuralMaths-Experiment-7b is a merge of the following models using LazyMergekit:

• WizardLM/WizardMath-7B-V1.1
• mlabonne/NeuralDaredevil-7B
• Kukedlc/Neural4gsm8k
• Eric111/Mayo
• Kukedlc/NeuralSirKrishna-7b

🧩 Configuration

models:
  - model: Kukedlc/NeuralSirKrishna-7b
    # No parameters necessary for base model
  - model: WizardLM/WizardMath-7B-V1.1
    parameters:
      density: 0.66
      weight: 0.2
  - model: mlabonne/NeuralDaredevil-7B
    parameters:
      density: 0.55
      weight: 0.2
  - model: Kukedlc/Neural4gsm8k
    parameters:
      density: 0.55
      weight: 0.2
  - model: Eric111/Mayo
    parameters:
      density: 0.44
      weight: 0.2
  - model: Kukedlc/NeuralSirKrishna-7b
    parameters:
      density: 0.66
      weight: 0.2
merge_method: dare_ties
base_model: Kukedlc/NeuralSirKrishna-7b
parameters:
  int8_mask: true
dtype: bfloat16

🌳 Model Family Tree

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuralMaths-Experiment-7b"
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