README.md

metadata

license: apache-2.0
tags:
  - moe
  - frankenmoe
  - merge
  - mergekit
  - lazymergekit
  - DeepMount00/Minerva-3B-base-RAG
  - FairMind/Minerva-3B-Instruct-v1.0
base_model:
  - DeepMount00/Minerva-3B-base-RAG
  - FairMind/Minerva-3B-Instruct-v1.0

Minerva-MoE-3x3B

Minerva-MoE-3x3B is a Mixture of Experts (MoE) made with the following models using LazyMergekit:

• DeepMount00/Minerva-3B-base-RAG
• FairMind/Minerva-3B-Instruct-v1.0

Evaluation

arc_it acc_norm: 31.91 hellaswag_it acc_norm: 52.20 mmmlu_it: 25.72

🧩 Configuration

base_model: sapienzanlp/Minerva-3B-base-v1.0
experts:
  - source_model: DeepMount00/Minerva-3B-base-RAG
    positive_prompts:
    - "rispondi a domande"
    - "cosa è"
    - "chi è"
    - "dove è"
    - "come si"
    - "spiegami"
    - "definisci"
  - source_model: FairMind/Minerva-3B-Instruct-v1.0
    positive_prompts:
    - "istruzione"
    - "input"
    - "risposta"
    - "scrivi"
    - "sequenza"
    - "istruzioni"
dtype: bfloat16

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "ludocomito/Minerva-MoE-3x3B"

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"])