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Model: ibndias/NeuralHermes-MoE-2x7B Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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tags:
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- merge
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model-index:
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- name: NeuralHermes-MoE-2x7B
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 62.12
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibndias/NeuralHermes-MoE-2x7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 84.21
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibndias/NeuralHermes-MoE-2x7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 64.56
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibndias/NeuralHermes-MoE-2x7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 43.61
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibndias/NeuralHermes-MoE-2x7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 78.14
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibndias/NeuralHermes-MoE-2x7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 51.86
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibndias/NeuralHermes-MoE-2x7B
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name: Open LLM Leaderboard
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---
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# NeuralHermes-MoE-2x7B
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This is a mix between teknium/OpenHermes-2.5-Mistral-7B and Intel/neural-chat-7b-v3-3.
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Using mistralai/Mistral-7B-v0.1 as the base model.
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This Mixture of Expert was done using `mergekit` method.
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# Getting Started
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```python
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import torch
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from transformers import pipeline
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pipe = pipeline("text-generation", model="ibndias/NeuralHermes-MoE-2x7B",torch_dtype=torch.bfloat16, device_map="auto")
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prompt = """<|system|> You are a helpful assistant.
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<|user|>
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Write me bash script to scan ip 192.3.1.4 with nmap only port that ends with 9 from 1-100.
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<|assistant|>
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"""
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outputs = pipe(prompt, max_new_tokens=512, do_sample=True, temperature=0.2, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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Output:
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><|system|> You are a helpful assistant.
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><|user|>
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>Write me bash script to scan ip 192.3.1.4 with nmap only port that ends with 9 from 1-100.
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><|assistant|>
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>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:
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>```
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>#!/bin/bash
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>IP_ADDRESS="192.3.1.4"
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>START_PORT=1
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>END_PORT=100
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>for ((i=$START_PORT; i<=$END_PORT; i++)); do
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> PORT=$i
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> if [[ $PORT % 10 == 9 ]]; then
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> nmap -p $PORT $IP_ADDRESS
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> fi
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>done
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>```
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>Save 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`.
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>...
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ibndias__NeuralHermes-MoE-2x7B)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |64.08|
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|AI2 Reasoning Challenge (25-Shot)|62.12|
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|HellaSwag (10-Shot) |84.21|
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|MMLU (5-Shot) |64.56|
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|TruthfulQA (0-shot) |43.61|
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|Winogrande (5-shot) |78.14|
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|GSM8k (5-shot) |51.86|
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