whatmodel.sh / Nous Hermes 2 Mistral 7B DPO
Nous Hermes 2 Mistral 7B DPO
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Nous Hermes 2 Mistral 7B DPO is an open-weights language model from Nous Research, released February 2024. Its published size is 7.24B parameters and a 33k-token context window. Licence: apache-2.0.
Catalogue identity notes
NousResearch/Nous-Hermes-2-Mistral-7B-DPO @ebec0a6 (created 2024-02-18, licence apache-2.0). Independent row: Open PT-LLM leaderboard average.
Model information sources
Release date ↗ · Weights licence ↗
HF safetensors metadata 7,241,748,480 parameters.
$ how it compares
The LLMs ranked either side of Nous Hermes 2 Mistral 7B DPO, out of 828 with a score, then more from Nous Research.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 619 |  Granite 4.0 H 1B | 40±1 | local | ||
| 620 |  Llama3-OpenBioLLM-8B | 40±2 | local | ||
| 621 | Nous Hermes 2 Mistral 7B DPO | 40±2 | local | ||
| 622 | Nous Hermes 2 Mixtral 8x7B DPO | 40±2 | local | ||
| 623 |  Deita 7B v1.0 | 40±2 | local | ||
| more from Nous Research | |||||
| 389 | Hermes 3 Llama 3.1 405B | 51±1 | local | ||
| 447 | Hermes 3 Llama 3.1 70B | 49±1 | local | ||
| 461 | Hermes 2 Theta Llama-3 70B | 48±2 | local | ||
$ what it's good at
other tests4 not in a task · 98% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| MedQA (USMLE, 4 options) | 53.1% ✎ | ▁▁··▄▁▁··▄▃█ beats 12/17 | 56% |
| MedMCQA | 49.0% ✎ | ▂▂··▄▄▂▂·▄██ beats 11/17 | 30% |
| Open PT-LLM Leaderboard average (9 Portuguese tasks) | 66.8% ✎ | ▃▁▁▁▁▁▁▁▂█▅▁ beats 129/276 | 9.5% |
| PubMedQA | 74.8% ✎ | ▂·▂▆·▄▂▄▄▂█▂ beats 15/17 | 3.2% |
$ where to run it
llama.cpp · recommended
- Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.macOS / Linux
curl -LsSf https://llama.app/install.sh | shWindowsirm https://llama.app/install.ps1 | iex - Run Nous Hermes 2 Mistral 7B DPO · needs ~4.37 GB · from huggingface.co/NousResearch (official)chat in the terminal
llama cli -hf NousResearch/Nous-Hermes-2-Mistral-7B-DPO-GGUF:Q4_K_M - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf NousResearch/Nous-Hermes-2-Mistral-7B-DPO-GGUF:Q4_K_M
LM Studio
- Install LM Studio · Open the app once before using the lms command.
- Run Nous Hermes 2 Mistral 7B DPO · needs ~4.37 GB · from huggingface.co/NousResearch (official)
- Or download it in the terminal
lms get https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO-GGUF@Q4_K_M -y - Then run lms ls to see its name, and lms chat followed by that name
Ollama
- Install Ollama
download it (macOS / Windows) ↗
Linuxcurl -fsSL https://ollama.com/install.sh | sh - Run Nous Hermes 2 Mistral 7B DPO · needs ~4.37 GB · from huggingface.co/NousResearch (official)chat in the terminal
ollama run hf.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO-GGUF:Q4_K_M
Official weights and documentation: huggingface.co/NousResearch ↗
$ how the score was worked out · Open Medical-LLM Leaderboard… 88%, Open Portuguese LLM Leaderboa… 9%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The 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's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 Open Medical-LLM Leaderboard results (HF dataset openlifescienceai/results) | 3 | 88% |
| Open Portuguese LLM Leaderboard (archived) | 1 | 9% |
| prior · pull toward the middle | 2% | |
| weighted average | = 40.2 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json