[whatmodel.sh](../../README.md) / Nous Hermes 2 Mistral 7B DPO # Nous Hermes 2 Mistral 7B DPO Nous Research · released 2024-02 · 7.24B params · apache-2.0 **score 42.3** ±2.5 · #529 of 732 · from 4 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 Nous Hermes 2 Mistral 7B DPO among every model on that task or test, worst to best.}

other tests (4)

test                                result  vs the others in its run    difficulty        counts  why
MedQA (USMLE, 4 options)             53.1%  ▁▁··▄▁▁··▄▃● beats 12/17    medium · avg 71%     54%  sharp · at its level
MedMCQA                              49.0%  ▂▂··▄▄▂▂·▄●█ beats 11/17    medium · avg 63%     25%  at its level
Open PT-LLM Leaderboard average (…   66.8%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 129/276  medium · avg 72%     15%  at its level · noisy
PubMedQA                             74.8%  ▂·▂▆·▄▂▄▄▂●▂ beats 15/17    medium · avg 75%    3.0%  blunt · at its level

## 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
Open Medical-LLM Leaderboard results        3   82% ▇▇▇▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard             1   15% ▇▇
prior: pull toward the middle                    3% ▇
weighted average                               = 42.3

_{Licences: Open Medical-LLM Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · 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 Mistral 7B DPO (Q4_K_M, needs ~4.37 GB, from [huggingface.co/NousResearch](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO-GGUF/tree/eb85cf06e8663157611e8ee472e61b43f50ee49f)) ```sh llama cli -hf NousResearch/Nous-Hermes-2-Mistral-7B-DPO-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf NousResearch/Nous-Hermes-2-Mistral-7B-DPO-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 Nous Hermes 2 Mistral 7B DPO (Q4_K_M, needs ~4.37 GB, from [huggingface.co/NousResearch](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO-GGUF/tree/eb85cf06e8663157611e8ee472e61b43f50ee49f)) ```sh lms get https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO-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 Nous Hermes 2 Mistral 7B DPO (Q4_K_M, needs ~4.37 GB, from [huggingface.co/NousResearch](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO-GGUF/tree/eb85cf06e8663157611e8ee472e61b43f50ee49f)) ```sh ollama run hf.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO-GGUF:Q4_K_M ``` Model repository and documentation: [huggingface.co/NousResearch](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO) [interactive version](https://whatmodel.sh/model/nous-hermes-2-mistral-7b-dpo) · [JSON](https://whatmodel.sh/api?format=json&all=1)