metadata
license: apache-2.0
tags:
- moe
- merge
- abideen/NexoNimbus-7B
- mlabonne/NeuralMarcoro14-7B
language:
- en
library_name: transformers
NexoNimbus-MoE-2x7B
NexoNimbus-MoE-2x7B is a Mixure of Experts (MoE) made with the following models:
🏆 Evaluation NexoNimbus-MoE-2x7B is the 10th best-performing 13B LLM on the Open LLM Leaderboard:
Task | Version | Metric | Value | Stderr | |
---|---|---|---|---|---|
arc_challenge | 0 | acc | 62.28 | ± | 1.41 |
acc_norm | 66.80 | ± | 1.37 | ||
hellaswag | 0 | acc | 66.83 | ± | 0.46 |
acc_norm | 85.66 | ± | 0.34 | ||
gsm8k | 0 | acc | 53.52 | ± | 1.37 |
winogrande | 0 | acc | 81.53 | ± | 1.09 |
mmlu | 0 | acc | 64.51 | ± | 1.00 |
Average: 67.51%
TruthfulQA
Task | Version | Metric | Value | Stderr | |
---|---|---|---|---|---|
truthfulqa_mc | 1 | mc1 | 35.98 | ± | 1.68 |
mc2 | 53.05 | ± | 1.53 |
🧩 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
Here's a Colab notebook to run NexoNimbus-MoE-2x7B in 4-bit precision on a free T4 GPU.
!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 is machine learning."}]
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"])