jsfs11_-_MixtureofMerges-MoE-2x7bRP-v8-gguf

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MixtureofMerges-MoE-2x7bRP-v8 - GGUF

• Model creator: https://huggingface.co/jsfs11/
• Original model: https://huggingface.co/jsfs11/MixtureofMerges-MoE-2x7bRP-v8/

Original model description:

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:

• ChaoticNeutrals/RP_Vision_7B
• ResplendentAI/DaturaCookie_7B
• This model is prone to NSFW outputs.

🧩 Configuration

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

!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

Detailed results can be found here