Instructions to use abideen/NexoNimbus-MoE-2x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abideen/NexoNimbus-MoE-2x7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="abideen/NexoNimbus-MoE-2x7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abideen/NexoNimbus-MoE-2x7B")
model = AutoModelForCausalLM.from_pretrained("abideen/NexoNimbus-MoE-2x7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use abideen/NexoNimbus-MoE-2x7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abideen/NexoNimbus-MoE-2x7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abideen/NexoNimbus-MoE-2x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abideen/NexoNimbus-MoE-2x7B

SGLang

How to use abideen/NexoNimbus-MoE-2x7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "abideen/NexoNimbus-MoE-2x7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abideen/NexoNimbus-MoE-2x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "abideen/NexoNimbus-MoE-2x7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abideen/NexoNimbus-MoE-2x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use abideen/NexoNimbus-MoE-2x7B with Docker Model Runner:
```
docker model run hf.co/abideen/NexoNimbus-MoE-2x7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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 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."

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Safetensors

Model size

13B params

Tensor type

BF16

Model tree for abideen/NexoNimbus-MoE-2x7B

Quantizations

2 models

Collection including abideen/NexoNimbus-MoE-2x7B

🛸 Mixture of Experts

Collection

Models and Code of different MoE merges. • 2 items • Updated Mar 22, 2024 • 1