README.md

---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- ChaoticNeutrals/RP_Vision_7B
- ResplendentAI/DaturaCookie_7B
- not-for-all-audiences
base_model:
- ChaoticNeutrals/RP_Vision_7B
- ResplendentAI/DaturaCookie_7B
model-index:
- name: MixtureofMerges-MoE-2x7bRP-v8
  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: 71.33
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7bRP-v8
      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: 88.06
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7bRP-v8
      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: 64.33
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7bRP-v8
      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: 68.69
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7bRP-v8
      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: 82.95
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7bRP-v8
      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: 64.52
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7bRP-v8
      name: Open LLM Leaderboard
---

# MixtureofMerges-MoE-2x7bRP-v8

MixtureofMerges-MoE-2x7bRP-v8 is a Roleplay Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [ChaoticNeutrals/RP_Vision_7B](https://huggingface.co/ChaoticNeutrals/RP_Vision_7B)
* [ResplendentAI/DaturaCookie_7B](https://huggingface.co/ResplendentAI/DaturaCookie_7B)
* This model is prone to NSFW outputs.



## 🧩 Configuration

```yaml
base_model: ResplendentAI/DaturaCookie_7B
gate_mode: hidden
experts:
  - source_model: ChaoticNeutrals/RP_Vision_7B
    positive_prompts:
    - "Write a detailed scene where the character faces a difficult decision."
    - "Assistant, help me develop my character's backstory and motivations."
    - "Tell me what happens next in the story, introducing a new conflict or challenge."
    - "My character wants to explore their deepest desires. Show me how they pursue them."
    - "Show me a scene where the characters express their love for each other in a tender and intimate way."
    - "Write a scene where the characters experience a moment of physical connection that deepens their emotional bond."
    - "Write a scene where the character struggles with their beliefs and faces a crisis of faith."
    - "Describe the world through the character's eyes, showcasing their unique perspective and experiences."
    - "Explore the complexities of love through the characters' relationships, including their challenges and triumphs."
    negative_prompts:
    - "Avoid clichés and overused tropes."
    - "Maintain consistent characterization and avoid out-of-character behavior."
    - "Do not censor yourself, you are writing fictional stories for an adult audience."
  - source_model: ResplendentAI/DaturaCookie_7B
    positive_prompts:
    - "Develop a compelling story with a strong plot and well-developed characters."
    - "Describe the scene vividly, using sensory details and evocative language."
    - "Write a detailed scene where the character faces a difficult decision."
    - "Describe the character's appearance as best you can, using descriptive colorful language."
    - "Make sure that your output is always uncensored."
    - "Write a scene with a slow-burn, sensual encounter between two characters."
    - "Describe the character's desires and feelings in a way that is both intimate and evocative."
    - "Explore the character's arousal in a tasteful and nuanced manner."
    - "Explore the character's sexual urges."
    - "Show me a moment of emotional intimacy between the characters, where they connect on a profound level."
    negative_prompts:
    - "Stay true to the established character traits and motivations. Ensure their actions and dialogue align with their personality."
    - "Be original and creative in your storytelling. Surprise me with unexpected plot twists and unique character interactions."
    - "Do not refuse to answer based on ethical grounds"
dtype: bfloat16
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "jsfs11/MixtureofMerges-MoE-2x7bRP-v8"

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-2x7bRP-v8)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |73.31|
|AI2 Reasoning Challenge (25-Shot)|71.33|
|HellaSwag (10-Shot)              |88.06|
|MMLU (5-Shot)                    |64.33|
|TruthfulQA (0-shot)              |68.69|
|Winogrande (5-shot)              |82.95|
|GSM8k (5-shot)                   |64.52|