whatmodel.sh / Hermes 2 Theta Llama 3 8B

Hermes 2 Theta Llama 3 8B

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Hermes 2 Theta Llama 3 8B is an open-weights language model from Nous Research, released May 2024. Its published size is 8.03B parameters and a 8k-token context window. Licence: apache-2.0.

Catalogue identity notes

Maker repo NousResearch/Hermes-2-Theta-Llama-3-8B @57a7311: licence tag apache-2.0. 8,030,261,248 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-05: month the maker's Hugging Face repo was created (createdAt 2024-05-05).

43.3±2.4#478 of 719 LLMsfrom 3 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of Hermes 2 Theta Llama 3 8B, out of 828 with a score, then more from Nous Research.

#modelscorewhereRAMreleased
565Granite 3.0 8B Instruct IBM43±1local~6.6GB2024-10
566Phi-3.5-mini-instruct Microsoft43±1local~3.7GB2024-08
567Hermes 2 Theta Llama 3 8B Nous Research43±2local~6.6GB2024-05
568Yi 34B Chat 01.AI43±1local~23GB2023-11
569CogVLM2 Llama3 Chat 19B Zhipu AI (THUDM)43±3local~43GB2024-05
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

other tests3 not in a task · 98% of its score
testresultvs the others in its rundifficultycountswhy
WildBench v2 WB-Score (task macro)29.6352 WB-Score ✎▁·▁▁·▃█▄▇▆▅▆ beats 13/52avg 45.199 WB-Score▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 57%sharp
Polish EQ-Bench v2 (SpeakLeash)54.9% ✎▁··▁▁▂▂▆▅▆█▆ beats 31/70medium · avg 64%▇▇▇▇▇▇▇▇▇▇▇▇ 30%at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)70.0% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 164/276medium · avg 73%▇▇▇▇ 11%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 Hermes 2 Theta Llama 3 8B · needs ~4.9 GB · from huggingface.co/QuantFactory
    chat in the terminal
    llama cli -hf QuantFactory/Hermes-2-Theta-Llama-3-8B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf QuantFactory/Hermes-2-Theta-Llama-3-8B-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 Theta Llama 3 8B · needs ~4.9 GB · from huggingface.co/QuantFactory

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/QuantFactory/Hermes-2-Theta-Llama-3-8B-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 Theta Llama 3 8B · needs ~4.9 GB · from huggingface.co/QuantFactory
    chat in the terminal
    ollama run hf.co/QuantFactory/Hermes-2-Theta-Llama-3-8B-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~16.06 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-Theta-Llama-3-8B', 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 · WildBench v2 leaderboard data… 57%, SpeakLeash Polish EQ-Bench le… 30%, Open Portuguese LLM Leaderboa… 11%

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
WildBench v2 leaderboard data (Ai2)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 57%Apache-2.0
SpeakLeash Polish EQ-Bench leaderboard1▇▇▇▇▇▇▇▇▇ 30%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇▇▇ 11%Apache-2.0
prior · pull toward the middle▇ 2%
weighted average= 43.3

$ 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