[whatmodel.sh](../../README.md) / Hermes 3 Llama 3.1 70B # Hermes 3 Llama 3.1 70B Nous Research · released 2024-07 · 70.55B params · llama3 **score 50.4** ±1.0 · #365 of 732 · from 14 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Hermes 3 Llama 3.1 70B among every model on that task or test, worst to best. 
task            vs the others      rank  counts  tests
chat & writing  █········●·█   #2 of 3*    2.0%      1
chat & writing · #2 of 3 · 2.0% of its score 
test                                result  vs the others in its run   difficulty      counts  why
IFEval (aggregation unspecified) …   81.2%  ▁▂▄▂▂▄●██▅▂▁ beats 55/145  easy · avg 77%    2.0%  small run
other tests (13) 
test                               result  vs the others in its run    difficulty        counts  why
LiveBench 2024-07-26                40.1%  ▁▁▁▂▂▃▂█▆●▅▃ beats 44/61    medium · avg 38%     44%  at its level
BIG-Bench Hard (maker)              67.8%  ▁▁▁▁▂▄▅▆●▃▃▁ beats 107/155  medium · avg 68%    9.2%  sharp · at its level · small run
GPQA (subset unspecified) (maker)   37.7%  ▂▁▂▂▄▃●▃▅█▃▃ beats 33/106   hard · avg 38%      8.9%  sharp · small run
MMLU (maker)                        79.1%  ▁▁▁▁▁▂▂▅██●▄ beats 164/214  easy · avg 78%      6.9%  at its level · small run
ARC Challenge (maker)               65.5%  ▄▁▁▂▁▄█▇●▂▃▂ beats 113/140  medium · avg 67%    6.8%  sharp · at its level · small run
MMLU-Pro (maker)                    47.2%  ▁▁·▁▁▁●▂▄▅█▁ beats 42/188   medium · avg 48%    5.5%  at its level · small run
WinoGrande (maker)                  83.2%  ▁▁▁▁▂▁▁▅▇█▃● beats 128/131  easy · avg 83%      3.9%  at its level · small run
OpenBookQA (maker)                  49.4%  ▅▁▁▁▂▂▃●▃▁▃▁ beats 69/107   hard · avg 49%      3.5%  at its level · small run
HellaSwag (maker)                   88.2%  ▁▁▁▂▂▂▄▆█▇▄● beats 132/135  easy · avg 87%      3.0%  small run
BoolQ (maker)                       88.0%  ▁····▁▂█▆▅▆● beats 98/103   easy · avg 88%      2.8%  small run
MuSR (maker)                        50.7%  █······█···● beats 2/2      medium · avg 51%    1.7%  blunt · at its level · small run
PIQA (maker)                        84.4%  ▁▁▁▂▂▃▅█▅▃●▁ beats 119/123  easy · avg 85%      1.1%  blunt · at its level · small run
TruthfulQA MC2 (maker)              63.3%  █▇▇▄▂▄▁●▅▄▁▁ beats 57/79    medium · avg 59%    0.3%  blunt · at its level · small run
## 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
Nous Research vendor-reported scores fo…       13   55% ▇▇▇▇▇▇▇
LiveBench model judgments                       1   44% ▇▇▇▇▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 50.4
Licences: LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) ## 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 3 Llama 3.1 70B (Q4_K_M, needs ~42.52 GB, from [huggingface.co/NousResearch](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-70B-GGUF/tree/2426f0fa281115286ea90967a480666fa502bade)) ```sh llama cli -hf NousResearch/Hermes-3-Llama-3.1-70B-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-3-Llama-3.1-70B-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 3 Llama 3.1 70B (Q4_K_M, needs ~42.52 GB, from [huggingface.co/NousResearch](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-70B-GGUF/tree/2426f0fa281115286ea90967a480666fa502bade)) ```sh lms get https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-70B-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 3 Llama 3.1 70B (Q4_0, needs ~40 GB, from [ollama.com](https://ollama.com/library/hermes3/tags)) ```sh ollama run hermes3:70b ``` Model repository and documentation: [huggingface.co/NousResearch](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-70B) [interactive version](https://whatmodel.sh/model/hermes-3-llama-3-1-70b) · [JSON](https://whatmodel.sh/api?format=json&all=1)