[whatmodel.sh](../../README.md) / Nous Hermes 2 Mixtral 8x7B DPO
# Nous Hermes 2 Mixtral 8x7B DPO
Nous Research · released 2024-01 · 46.7B params (12.9B active) · apache-2.0
**score 41.9** ±1.7 · #534 of 732 · from 4 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 Nous Hermes 2 Mixtral 8x7B DPO among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #346 of 402* 59% 2
chat & writing · #346 of 402 · 59% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank higher overall. Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1164.5 ▁▁▁▂●▃▃▆▆▇█▃ beats 46/385 avg 1260 31% blunt
Polyrating rating (bias-aware re-… 1090.0 ▂▁▂▁▃▇●▆▇▄▂▂ beats 41/95 avg 1187 28% typical
other tests (2)
test result vs the others in its run difficulty counts why
WildBench v2 WB-Score (task macro) 30.7114 WB-Score ▁·▁▁·▃█●▇▆▅▆ beats 17/52 avg 43.019 WB-Score 33% sharp
Open PT-LLM Leaderboard average (… 67.2% ▃▁▁▁▁▁▁▁▂●▅▁ beats 133/276 medium · avg 72% 7.1% at its level · noisy
source results share of the score
WildBench v2 leaderboard data 1 33% ▇▇▇▇
LMArena leaderboard dataset 1 31% ▇▇▇▇
Polyrating: A Cost-Effective and Bias-A… 1 28% ▇▇▇
Open Portuguese LLM Leaderboard 1 7% ▇
prior: pull toward the middle 1% ▇
weighted average = 41.9
Licences: WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · 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 Nous Hermes 2 Mixtral 8x7B DPO (Q4_K_M, needs ~28.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF/tree/38d3991e079f5bee1486a66a4217468721633a26))
```sh
llama cli -hf TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF:Q4_K_M
```
Other versions: Q8_0 (~49.6 GB) `llama cli -hf TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-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 Nous Hermes 2 Mixtral 8x7B DPO (Q4_K_M, needs ~28.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF/tree/38d3991e079f5bee1486a66a4217468721633a26))
```sh
lms get https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-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 (~49.6 GB) `lms get https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF@Q8_0 -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 Nous Hermes 2 Mixtral 8x7B DPO (Q4_K_M, needs ~28.52 GB, from [ollama.com](https://ollama.com/library/nous-hermes2-mixtral/tags))
```sh
ollama run nous-hermes2-mixtral:8x7b-dpo-q4_K_M
```
Other versions: Q8_0 (~56.54 GB) `ollama run nous-hermes2-mixtral:8x7b-dpo-q8_0`
Model repository and documentation: [huggingface.co/NousResearch](https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO)
[interactive version](https://whatmodel.sh/model/nous-hermes-2-mixtral-8x7b-dpo) · [JSON](https://whatmodel.sh/api?format=json&all=1)