[whatmodel.sh](../../README.md) / Hermes 2 Theta Llama-3 70B # Hermes 2 Theta Llama-3 70B Nous Research · released 2024-06 · 70.55B params · llama3 **score 48.8** ±1.9 · #394 of 732 · from 3 benchmark results across 1 source Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Hermes 2 Theta Llama-3 70B among every model on that task or test, worst to best.}

task  vs the others          rank  counts  tests
math  ▁▁▃●█▆▄█▄▂▁▁   #344 of 431*     65%      2

math · #344 of 431 · 65% of its score

Beats [Athene-70B](athene-70b.md) here, though they rank higher overall. Trails [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md), [Zephyr 7B beta](zephyr-7b-beta.md) here, though they rank lower overall.

test                      result  vs the others in its run   difficulty            counts  why
MATH level 5               22.7%  ▂▂▅●▄█▆▃▅▂▃▄ beats 19/92   hard · avg 28%           59%  sharp
OTIS mock AIME 2024–2025    2.5%  ▃●▄▃▃▃▇▇█▃▂▅ beats 18/197  very hard · avg 7.2%    6.3%  sharp · too hard for it · noisy

other tests (1)

test          result  vs the others in its run   difficulty      counts  why
GPQA Diamond   37.5%  ▁▁▁▁▂●▆▄▅▇█▅ beats 32/211  hard · avg 39%     33%  sharp

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md)

source                         results  share of the score
Epoch AI                             3   98% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle             2% ▇
weighted average                        = 48.8

_{Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))} ## How to run it ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run Hermes 2 Theta Llama-3 70B (gguf-q4_k_m, needs ~42.83 GB, from [huggingface.co/NousResearch](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-70B-GGUF/tree/main)) ```sh llama cli -hf NousResearch/Hermes-2-Theta-Llama-3-70B-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf NousResearch/Hermes-2-Theta-Llama-3-70B-GGUF:Q4_K_M ``` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run Hermes 2 Theta Llama-3 70B (gguf-q4_k_m, needs ~42.83 GB, from [huggingface.co/NousResearch](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-70B-GGUF/tree/main)) ```sh lms get https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-70B-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Hermes 2 Theta Llama-3 70B (gguf-q4_k_m, needs ~42.83 GB, from [huggingface.co/NousResearch](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-70B-GGUF/tree/main)) ```sh ollama run hf.co/NousResearch/Hermes-2-Theta-Llama-3-70B-GGUF:Q4_K_M ``` Model repository and documentation: [huggingface.co/NousResearch](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-70B) [interactive version](https://whatmodel.sh/model/hermes-2-theta-llama-3-70b) · [JSON](https://whatmodel.sh/api?format=json&all=1)