MoEstral-2x7B

Are 2 models better than 1?

MoEstral-2x2B is a Mixure of Experts (MoE) made with the following models using mergekit:

• mistralai/Mistral-7B-Instruct-v0.2
• mistralai/Mistral-7B-Instruct-v0.2

🧩 Configuration

base_model: mistralai/Mistral-7B-Instruct-v0.2
gate_mode: cheap_embed
dtype: float16
experts:
  - source_model: mistralai/Mistral-7B-Instruct-v0.2
    positive_prompts: ["science, logic, math"]
  - source_model: mistralai/Mistral-7B-Instruct-v0.2
    positive_prompts: ["reasoning, numbers, abstract"]

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "paulilioaica/MoEstral-2x2B"

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

Metric	Value
Avg.	66.34
AI2 Reasoning Challenge (25-Shot)	65.10
HellaSwag (10-Shot)	84.82
MMLU (5-Shot)	61.62
TruthfulQA (0-shot)	62.72
Winogrande (5-shot)	78.37
GSM8k (5-shot)	45.41