Instructions to use ibndias/NeuralHermes-MoE-2x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibndias/NeuralHermes-MoE-2x7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ibndias/NeuralHermes-MoE-2x7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ibndias/NeuralHermes-MoE-2x7B") model = AutoModelForCausalLM.from_pretrained("ibndias/NeuralHermes-MoE-2x7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ibndias/NeuralHermes-MoE-2x7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ibndias/NeuralHermes-MoE-2x7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ibndias/NeuralHermes-MoE-2x7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ibndias/NeuralHermes-MoE-2x7B
- SGLang
How to use ibndias/NeuralHermes-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 "ibndias/NeuralHermes-MoE-2x7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ibndias/NeuralHermes-MoE-2x7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "ibndias/NeuralHermes-MoE-2x7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ibndias/NeuralHermes-MoE-2x7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ibndias/NeuralHermes-MoE-2x7B with Docker Model Runner:
docker model run hf.co/ibndias/NeuralHermes-MoE-2x7B
NeuralHermes-MoE-2x7B
This is a mix between teknium/OpenHermes-2.5-Mistral-7B and Intel/neural-chat-7b-v3-3. Using mistralai/Mistral-7B-v0.1 as the base model.
This Mixture of Expert was done using mergekit method.
Getting Started
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="ibndias/NeuralHermes-MoE-2x7B",torch_dtype=torch.bfloat16, device_map="auto")
prompt = """<|system|> You are a helpful assistant.
<|user|>
Write me bash script to scan ip 192.3.1.4 with nmap only port that ends with 9 from 1-100.
<|assistant|>
"""
outputs = pipe(prompt, max_new_tokens=512, do_sample=True, temperature=0.2, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Output:
<|system|> You are a helpful assistant. <|user|> Write me bash script to scan ip 192.3.1.4 with nmap only port that ends with 9 from 1-100. <|assistant|> Sure, here's a bash script that scans the specified IP address with nmap for open ports that end with 9 from 1 to 100:
#!/bin/bash IP_ADDRESS="192.3.1.4" START_PORT=1 END_PORT=100 for ((i=$START_PORT; i<=$END_PORT; i++)); do PORT=$i if [[ $PORT % 10 == 9 ]]; then nmap -p $PORT $IP_ADDRESS fi doneSave the script with a.sh extension (e.g., scan_ports.sh) and make it executable by running
chmod +x scan_ports.sh. Then, run the script by executing./scan_ports.sh. ...
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 64.08 |
| AI2 Reasoning Challenge (25-Shot) | 62.12 |
| HellaSwag (10-Shot) | 84.21 |
| MMLU (5-Shot) | 64.56 |
| TruthfulQA (0-shot) | 43.61 |
| Winogrande (5-shot) | 78.14 |
| GSM8k (5-shot) | 51.86 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard62.120
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard84.210
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.560
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard43.610
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard78.140
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard51.860