metadata
license: apache-2.0
tags:
- moe
- merge
- mergekit
- lazymergekit
- abideen/NexoNimbus-7B
- mlabonne/NeuralMarcoro14-7B
NexoNimbus-MoE-2x7B
NexoNimbus-MoE-2x7B is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
🧩 Configuration
base_model: teknium/OpenHermes-2.5-Mistral-7B
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: abideen/NexoNimbus-7B
positive_prompts:
- "Mathematics"
- "Physics"
- "Chemistry"
- "Biology"
- "Medicine"
- "Engineering"
- "Computer Science"
negative_prompts:
- "History"
- "Philosophy"
- "Linguistics"
- "Literature"
- "Art and Art History"
- "Music Theory and Composition"
- "Performing Arts (Theater, Dance)"
- source_model: mlabonne/NeuralMarcoro14-7B
positive_prompts:
- "Earth Sciences (Geology, Meteorology, Oceanography)"
- "Environmental Science"
- "Astronomy and Space Science"
- "Psychology"
- "Sociology"
- "Anthropology"
- "Political Science"
- "Economics"
negative_prompts:
- "Education"
- "Law"
- "Theology and Religious Studies"
- "Communication Studies"
- "Business and Management"
- "Agricultural Sciences"
- "Nutrition and Food Science"
- "Sports Science"
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "abideen/NexoNimbus-MoE-2x7B"
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"])