whatmodel.sh / Nous-Hermes-13b

Nous-Hermes-13b

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Nous-Hermes-13b is an open-weights language model from Nous Research, released June 2023. Licence: gpl.

Catalogue identity notes

Maker repo NousResearch/Nous-Hermes-13b @24e8c03: licence tag gpl. Released 2023-06: month the maker's Hugging Face repo was created (createdAt 2023-06-03).

33.9±2.2#610 of 719 LLMsfrom 3 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Nous-Hermes-13b, out of 828 with a score, then more from Nous Research.

#modelscorewhereRAMreleased
703Llama 2 13B (base) Meta34±1local~9.3GB2023-07
704Code Llama 7B (base) Meta34±1local~5.5GB2023-08
705Nous-Hermes-13b Nous Research34±2local~9.8GB2023-06
706ChatGLM3 6B 128K Zhipu AI (THUDM)34±4local~5.8GB2024-01
707Zephyr 7B beta Hugging Face34±1local~6.1GB2023-10
more from Nous Research
389Hermes 3 Llama 3.1 405B Nous Research51±1local~114GB2024-08
447Hermes 3 Llama 3.1 70B Nous Research49±1local~44GB2024-07
461Hermes 2 Theta Llama-3 70B Nous Research48±2local~47GB2024-06

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #379 of 393 * · 90% of its score · 2 tests

Beats Phi-2 (2.7B) here, though they rank higher overall. Trails Gemma 3 270M IT, SmolLM2 360M Instruct, Granite 4.0 350M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled9.7% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 21/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 69%typical
AlpacaEval 1.0 win rate (vs text-davinci-003, GPT-4 judge)65.5% ✎▂▂·▆·▇▃▆█▆▄▂ beats 11/45very easy · avg 96%▇▇▇▇▇▇▇▇▇ 21%sharp · at its level · noisy
other tests1 not in a task · 7.9% of its score
testresultvs the others in its rundifficultycountswhy
Open PT-LLM Leaderboard average (9 Portuguese tasks)55.7% ✎▃▁▁▁▁▁▁▁▃█▅▁ beats 77/276medium · avg 73%▇▇▇ 7.9%at its level · noisy

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Nous-Hermes-13b · needs ~7.9 GB · from huggingface.co/TheBloke
    chat in the terminal
    llama cli -hf TheBloke/Nous-Hermes-13B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf TheBloke/Nous-Hermes-13B-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Nous-Hermes-13b · needs ~7.9 GB · from huggingface.co/TheBloke

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/TheBloke/Nous-Hermes-13B-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Nous-Hermes-13b · needs ~7.9 GB · from huggingface.co/TheBloke
    chat in the terminal
    ollama run hf.co/TheBloke/Nous-Hermes-13B-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~26.03 GB · from huggingface.co/NousResearch (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='NousResearch/Nous-Hermes-13b', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/NousResearch ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 90%, Open Portuguese LLM Leaderboa… 8%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 90%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇▇ 8%Apache-2.0
prior · pull toward the middle▇ 2%
weighted average= 33.9

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json