README.md

---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- yam-peleg/Experiment26-7B
- mlabonne/AlphaMonarch-7B
base_model:
- yam-peleg/Experiment26-7B
- mlabonne/AlphaMonarch-7B
model-index:
- name: MixtureofMerges-MoE-2x7b-v6
  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: 73.38
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v6
      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: 89.16
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v6
      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: 64.53
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v6
      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: 78.58
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v6
      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: 84.77
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v6
      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: 69.37
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v6
      name: Open LLM Leaderboard
---

# MixtureofMerges-MoE-2x7b-v6

MixtureofMerges-MoE-2x7b-v6 is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)

## 🧩 Configuration

```yaml
base_model: yam-peleg/Experiment26-7B
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: yam-peleg/Experiment26-7B
    positive_prompts:
      - "Answer this question from the ARC (Argument Reasoning Comprehension)."
      - "Use common sense and logical reasoning skills."
      - "What assumptions does this argument rely on?"
      - "Are these assumptions valid? Explain."
      - "Could this be explained in a different way? Provide an alternative explanation."
      - "Identify any weaknesses in this argument."
      - "Does this argument contain any logical fallacies? If so, which ones?"
      - "Generate a few possible continuations to this scenario."
      - "Demonstrate understanding of everyday commonsense in your response."
      - "Use contextual clues to determine the most likely outcome."
      - "Continue this scenario, but make the writing style sound archaic and overly formal."
      - "This narrative is predictable. Can you introduce an unexpected yet plausible twist?"
      - "The character is angry. Continue this scenario showcasing a furious outburst."
    negative_prompts:
      - "misses key evidence"
      - "overly general"
      - "focuses on irrelevant details"
      - "assumes information not provided"
      - "relies on stereotypes"
      - "repetitive phrases"
      - "overuse of the same words"
      - "contradicts earlier statements - breaks the internal logic of the scenario"
      - "out of character dialogue"
      - "awkward phrasing - sounds unnatural"
      - "doesn't match the given genre"
  - source_model: mlabonne/AlphaMonarch-7B
    positive_prompts:
      - "Answer this question, demonstrating commonsense understanding and using any relevant general knowledge you may have."
      - "Provide a concise summary of this passage, then explain why the highlighted section is essential to the main idea."
      - "Read these two brief articles presenting different viewpoints on the same topic. List their key arguments and highlight where they disagree."
      - "Paraphrase this statement, changing the emotional tone but keeping the core meaning intact. Example: Rephrase a worried statement in a humorous way"
      - "Create a short analogy that helps illustrate the main concept of this article."
      - "Calculate the answer to this math problem"
      - "My mathematical capabilities are strong, allowing me to handle complex mathematical queries"
      - "solve for"
      - "A store sells apples at $0.50 each. If Emily buys 12 apples, how much does she need to pay?"
      - "Isolate x in the following equation: 2x + 5 = 17"
      - "Solve this equation and show your working."
      - "Explain why you used this formula to solve the problem."
      - "Attempt to divide this number by zero. Explain why this cannot be done."
    negative_prompts:
      - "sounds too basic"
      - "understated"
      - "dismisses important details"
      - "avoids the question's nuance"
      - "takes this statement too literally"
      - "incorrect"
      - "inaccurate"
      - "assumed without proof"
      - "rushed calculation"
      - "confuses mathematical concepts"
      - "draws illogical conclusions"
      - "circular reasoning"
```

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jsfs11/MixtureofMerges-MoE-2x7b-v6"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_jsfs11__MixtureofMerges-MoE-2x7b-v6)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |76.63|
|AI2 Reasoning Challenge (25-Shot)|73.38|
|HellaSwag (10-Shot)              |89.16|
|MMLU (5-Shot)                    |64.53|
|TruthfulQA (0-shot)              |78.58|
|Winogrande (5-shot)              |84.77|
|GSM8k (5-shot)                   |69.37|