whatmodel.sh / Hermes 2 Pro Mistral 7B

Hermes 2 Pro Mistral 7B

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Hermes 2 Pro Mistral 7B is an open-weights language model from Nous Research, released March 2024. Its published size is 7.24B parameters and a 33k-token context window. Licence: apache-2.0.

Catalogue identity notes

Maker repo NousResearch/Hermes-2-Pro-Mistral-7B @24dbda5: licence tag apache-2.0. 7,241,994,240 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-03: month the maker's Hugging Face repo was created (createdAt 2024-03-11).

42.0±6.4#669 of 949 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Hermes 2 Pro Mistral 7B, out of 1132 with a score, then more from Nous Research.

#modelscorewhereRAMreleased
827Claude Instant 1.2 Anthropic42±1comparison only2023-08
828Llama 3 8B Instruct Meta42±0local~6.6GB2024-04
829Hermes 2 Pro Mistral 7B Nous Research42±6local~4.9GB2024-03
830Dorna-Llama3-8B-Instruct Part AI42±5local~6.6GB2024-06
831WizardLM 70B V1.0 Microsoft42±1local~32GB2023-08
more from Nous Research
534Hermes 3 Llama 3.1 405B Nous Research51±1local~114GB2024-08
585Nous Hermes 2 Yi 34B Nous Research50±7local~22GB2023-12
624Hermes 3 Llama 3.1 70B Nous Research49±1local~44GB2024-07

see all LLMs · find the ones that fit your machine

$ what it's good at

agent tasks▁▁▃▆█▆▅▄▄▃▂▁ #311 of 334 * · 81% of its score · 1 test

Beats Bielik 11B v2.3 Instruct, GLM-4 9B Chat, Command R (08-2024) here, though they rank higher overall. Trails Granite 4.0 H 350M, Qwen3 0.6B, Qwen2.5 1.5B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
BFCL v341.3% ✎▁▁▁▁▁▃▅█▅▄▁▁ beats 18/114medium · avg 52%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 81%blunt · at its level

$ 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 Hermes 2 Pro Mistral 7B · needs ~4.4 GB · from huggingface.co/backyardai
    chat in the terminal
    llama cli -hf backyardai/Hermes-2-Pro-Mistral-7B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf backyardai/Hermes-2-Pro-Mistral-7B-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 Hermes 2 Pro Mistral 7B · needs ~4.4 GB · from huggingface.co/backyardai

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/backyardai/Hermes-2-Pro-Mistral-7B-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 Hermes 2 Pro Mistral 7B · needs ~4.4 GB · from huggingface.co/backyardai
    chat in the terminal
    ollama run hf.co/backyardai/Hermes-2-Pro-Mistral-7B-GGUF:Q4_K_M

MLX (Mac)

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Hermes 2 Pro Mistral 7B · needs ~3.2 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/Hermes-2-Pro-Mistral-7B-3bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Hermes-2-Pro-Mistral-7B-3bit

Other ways to run it

  1. Run it as published · needs ~14.48 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/Hermes-2-Pro-Mistral-7B', 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 · Berkeley Function Calling Lea… 81%

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
Berkeley Function Calling Leaderboard result archive1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 81%Apache-2.0
prior · pull toward the middle▇▇▇▇▇▇ 19%
weighted average= 42.0

$ improve this page

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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