MixtureofMerges-MoE-4x7b-v3

MixtureofMerges-MoE-4x7b-v3

MixtureofMerges-MoE-4x7b-v3 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

• jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES
• senseable/WestLake-7B-v2
• mlabonne/OmniBeagle-7B
• vanillaOVO/supermario_v3

🧩 Configuration

base_model: senseable/WestLake-7B-v2
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES
    positive_prompts:
      - "Answer this question from the ARC (Argument Reasoning Comprehension)."
      - "Use common sense and logical reasoning skills."
    negative_prompts:
      - "nonsense"
      - "irrational"
      - "math"
      - "code"
  - source_model: senseable/WestLake-7B-v2
    positive_prompts:
      - "Answer this question from the Winogrande test."
      - "Use advanced knowledge of culture and humanity"
    negative_prompts:
      - "ignorance"
      - "uninformed"
      - "creativity"
  - source_model: mlabonne/OmniBeagle-7B
    positive_prompts:
      - "Calculate the answer to this math problem"
      - "My mathematical capabilities are strong, allowing me to handle complex mathematical queries"
      - "solve for"
    negative_prompts:
      - "incorrect"
      - "inaccurate"
      - "creativity"
  - source_model: vanillaOVO/supermario_v3
    positive_prompts:
      - "Predict the most plausible continuation for this scenario."
      - "Demonstrate understanding of everyday commonsense in your response."
      - "Use contextual clues to determine the most likely outcome."
      - "Apply logical reasoning to complete the given narrative."
      - "Infer the most realistic action or event that follows."
    negative_prompts:
      - "guesswork"
      - "irrelevant information"
      - "contradictory response"
      - "illogical conclusion"
      - "ignoring context"

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jsfs11/MixtureofMerges-MoE-4x7b-v3"

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