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---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- moe
- merge
- abideen/NexoNimbus-7B
- mlabonne/NeuralMarcoro14-7B
model-index:
- name: NexoNimbus-MoE-2x7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 66.81
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/NexoNimbus-MoE-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 85.66
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/NexoNimbus-MoE-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.51
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/NexoNimbus-MoE-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 53.06
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/NexoNimbus-MoE-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 81.53
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/NexoNimbus-MoE-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 53.53
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/NexoNimbus-MoE-2x7B
name: Open LLM Leaderboard
---
# NexoNimbus-MoE-2x7B
NexoNimbus-MoE-2x7B is a Mixure of Experts (MoE) made with the following models:
* [abideen/NexoNimbus-7B](https://huggingface.co/abideen/NexoNimbus-7B)
* [mlabonne/NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B)
🏆 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
```yaml
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](https://colab.research.google.com/drive/1B1Q7vO95cDkEJbKIPhOWr6exB9-Q_lr-?usp=sharing) to run NexoNimbus-MoE-2x7B in 4-bit precision on a free T4 GPU.
```python
!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 data science."}]
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"])
```
"Data science is an interdisciplinary field that combines mathematics, statistics, computer science, and domain expertise in order to extract meaningful insights and knowledge from structured and unstructured data. It involves the process of collecting, cleaning, transforming, analyzing, and visualizing data in order to identify patterns, trends, and relationships that can inform decision-making and drive business strategies. Data scientists use various tools and techniques, such as machine learning, deep learning, and natural language processing, to develop predictive models, optimize processes, and automate decision-making. The field of data science is rapidly evolving as more and more data is generated and the demand for data-driven insights continues to grow."
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_abideen__NexoNimbus-MoE-2x7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |67.51|
|AI2 Reasoning Challenge (25-Shot)|66.81|
|HellaSwag (10-Shot) |85.66|
|MMLU (5-Shot) |64.51|
|TruthfulQA (0-shot) |53.06|
|Winogrande (5-shot) |81.53|
|GSM8k (5-shot) |53.53|
|