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metadata
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
license: apache-2.0
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
πŸ€– NeuralMaths-Experiment-7b πŸ€–
πŸ” Number One in GSM8K LeaderBoard! πŸ†

image/png

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

🧩 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

πŸ’» 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"])