metadata

license: cc-by-nc-4.0
tags:
  - moe
  - frankenmoe
  - merge
  - mergekit
  - lazymergekit
  - autoquant
  - gguf
base_model:
  - mlabonne/AlphaMonarch-7B
  - beowolx/CodeNinja-1.0-OpenChat-7B
  - SanjiWatsuki/Kunoichi-DPO-v2-7B
  - mlabonne/NeuralDaredevil-7B

🔮 Beyonder-4x7B-v3

Beyonder-4x7B-v3 is an improvement over the popular Beyonder-4x7B-v2. It's a Mixture of Experts (MoE) made with the following models using LazyMergekit:

🔍 Applications

This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio).

If you use SillyTavern, you might want to tweak the inference parameters. Here's what LM Studio uses as a reference: temp 0.8, top_k 40, top_p 0.95, min_p 0.05, repeat_penalty 1.1.

Thanks to its four experts, it's a well-rounded model, capable of achieving most tasks. As two experts are always used to generate an answer, every task benefits from other capabilities, like chat with RP, or math with code.

⚡ Quantized models

GGUF: https://huggingface.co/mlabonne/Beyonder-4x7B-v3-GGUF

🏆 Evaluation

Nous

Beyonder-4x7B-v3 is one of the best models on Nous' benchmark suite (evaluation performed using LLM AutoEval) and significantly outperforms the v2. See the entire leaderboard here.

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
mlabonne/AlphaMonarch-7B 📄	62.74	45.37	77.01	78.39	50.2
mlabonne/Beyonder-4x7B-v3 📄	61.91	45.85	76.67	74.98	50.12
mlabonne/NeuralDaredevil-7B 📄	59.39	45.23	76.2	67.61	48.52
mlabonne/Beyonder-4x7B-v2 📄	57.13	45.29	75.95	60.86	46.4

Open LLM Leaderboard

Running...

🧩 Configuration

base_model: mlabonne/AlphaMonarch-7B
experts:
  - source_model: mlabonne/AlphaMonarch-7B
    positive_prompts:
    - "chat"
    - "assistant"
    - "tell me"
    - "explain"
    - "I want"
  - source_model: beowolx/CodeNinja-1.0-OpenChat-7B
    positive_prompts:
    - "code"
    - "python"
    - "javascript"
    - "programming"
    - "algorithm"
  - source_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
    positive_prompts:
    - "storywriting"
    - "write"
    - "scene"
    - "story"
    - "character"
  - source_model: mlabonne/NeuralDaredevil-7B
    positive_prompts:
    - "reason"
    - "math"
    - "mathematics"
    - "solve"
    - "count"

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Beyonder-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"])

Output:

A Mixture of Experts (MoE) is a neural network architecture that tackles complex tasks by dividing them into simpler subtasks, delegating each to specialized expert modules. These experts learn to independently handle specific problem aspects. The MoE structure combines their outputs, leveraging their expertise for improved overall performance. This approach promotes modularity, adaptability, and scalability, allowing for better generalization in various applications.