[whatmodel.sh](../../README.md) / Hermes 3 Llama 3.1 8B
# Hermes 3 Llama 3.1 8B
Nous Research · released 2024-07 · 8.03B params · llama3
**score 43.7** ±1.1 · #506 of 732 · from 16 benchmark results across 4 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 8B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #366 of 469* 23% 1
chat & writing ●········█·█ #3 of 3* 2.8% 1
coding · #366 of 469 · 23% of its score
Beats [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md) here, though they rank higher overall. Trails [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [Code Tulu 2 13B](code-tulu-2-13b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Aider code editing (133 Exercism … 30.1% ▁·▁●▄▁▆▄█▆▂▂ beats 2/62 medium · avg 51% 23% sharp · at its level
chat & writing · #3 of 3 · 2.8% of its score
test result vs the others in its run difficulty counts why
IFEval (aggregation unspecified) … 62.3% ▁▂▄●▂▄▆██▅▂▁ beats 23/145 easy · avg 77% 2.8% at its level · small run
other tests (14)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 52.9% ▁▁▁▁▂▄▅●█▃▃▁ beats 73/155 medium · avg 68% 11% sharp · at its level · small run
MMLU (maker) 64.8% ▁▁▁▁▁▂▂▅●█▆▄ beats 104/214 easy · avg 78% 10% at its level · small run
ARC Challenge (maker) 58.1% ▄▁▁▂▁▄█●▃▂▃▂ beats 104/140 medium · avg 67% 8.3% sharp · at its level · small run
GPQA (subset unspecified) (maker) 29.4% ▂▁▂▂●▃▆▃▅█▃▃ beats 17/106 hard · avg 38% 7.2% sharp · small run
Open Persian LLM Leaderboard v2 a… 50.4% ▂▄▂▃●▄▃▆▃▅▂▂ beats 31/81 medium · avg 57% 6.4% blunt · at its level
MMLU-Pro (maker) 32.1% ▁▁·▁▁●▃▂▄▅█▁ beats 15/188 medium · avg 48% 6.1% at its level · small run
WinoGrande (maker) 77.7% ▁▁▁▁▂▁▁▅▇█●▁ beats 123/131 easy · avg 83% 5.3% at its level · small run
HellaSwag (maker) 82.8% ▁▁▁▂▂▂▄▆█▇●▂ beats 120/135 easy · avg 87% 4.5% small run
BoolQ (maker) 85.0% ▁····▁▂█▆▅▆● beats 92/103 easy · avg 88% 4.0% at its level · small run
OpenBookQA (maker) 47.8% ▅▁▁▁▂▂▃●▃▁▃▁ beats 61/107 hard · avg 49% 3.7% small run
Open PT-LLM Leaderboard average (… 68.7% ▃▁▁▁▁▁▁▁▂●▅▁ beats 154/276 medium · avg 72% 3.2% at its level · noisy
MuSR (maker) 43.5% ●······█···█ beats 0/2 medium · avg 51% 2.1% blunt · at its level · small run
PIQA (maker) 80.3% ▁▁▁▂▂▃▅█●▃▁▁ beats 95/123 easy · avg 85% 1.4% blunt · at its level · small run
TruthfulQA MC2 (maker) 58.7% █▇▇▄▂▄●▆▅▄▁▁ beats 51/79 medium · avg 59% 0.4% blunt · at its level · small run
source results share of the score
Nous Research vendor-reported scores fo… 13 66% ▇▇▇▇▇▇▇▇
Aider code editing benchmark runs 1 23% ▇▇▇
Open Persian LLM Leaderboard v2 1 6% ▇
Open Portuguese LLM Leaderboard 1 3% ▇
prior: pull toward the middle 1% ▇
weighted average = 43.7
Licences: Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))
## 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 8B (Q4_K_M, needs ~4.9 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Hermes-3-Llama-3.1-8B-GGUF/tree/223c8e24dec0f863b6abe4c89bf20f6941b805e2))
```sh
llama cli -hf bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.5 GB) `llama cli -hf bartowski/Hermes-3-Llama-3.1-8B-GGUF:Q8_0`
### 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 8B (Q4_K_M, needs ~4.9 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Hermes-3-Llama-3.1-8B-GGUF/tree/223c8e24dec0f863b6abe4c89bf20f6941b805e2))
```sh
lms get https://huggingface.co/bartowski/Hermes-3-Llama-3.1-8B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~8.5 GB) `lms get https://huggingface.co/bartowski/Hermes-3-Llama-3.1-8B-GGUF@Q8_0 -y` · 8bit (~8.5 GB, Mac only) `lms get https://huggingface.co/Shaund91/Hermes-3-Llama-3.1-8B-mlx-8Bit -y`
### 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 8B (Q4_K_M, needs ~5.32 GB, from [ollama.com](https://ollama.com/library/hermes3/tags))
```sh
ollama run hermes3:8b-llama3.1-q4_K_M
```
Other versions: Q8_0 (~10.14 GB) `ollama run hermes3:8b-llama3.1-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Hermes 3 Llama 3.1 8B (8bit, needs ~8.5 GB, Mac only, from [huggingface.co/Shaund91](https://huggingface.co/Shaund91/Hermes-3-Llama-3.1-8B-mlx-8Bit/tree/835711676d37a6e55ab19b22f60bf7ebb6e778c9))
```sh
mlx_lm.chat --model Shaund91/Hermes-3-Llama-3.1-8B-mlx-8Bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model Shaund91/Hermes-3-Llama-3.1-8B-mlx-8Bit
```
Model repository and documentation: [huggingface.co/NousResearch](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B)
[interactive version](https://whatmodel.sh/model/hermes-3-llama-3-1-8b) · [JSON](https://whatmodel.sh/api?format=json&all=1)