[whatmodel.sh](../../README.md) / Gemma 2 9B IT SimPO
# Gemma 2 9B IT SimPO
Princeton NLP · released 2024-07 · 9.24B params · mit
**score 59.9** ±1.2 · #203 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 Gemma 2 9B IT SimPO among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆▄▆●▆▇▇▃ #225 of 402* 80% 2
chat & writing · #225 of 402 · 80% of its score
Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [Olmo 3 7B Instruct](olmo-3-7b-instruct.md), [Granite 4.0 H Micro 3B](granite-4-0-h-micro.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 72.4% ▁▂▂▃▆▆█▆▄▄▁● beats 121/122 medium · avg 35% 64% sharp · at its level
LMArena (text) 1280.4 ▁▁▁▂▃▃●▆▆▇█▃ beats 111/385 avg 1260 16% blunt
other tests (2)
test result vs the others in its run difficulty counts why
WildBench v2 WB-Score (task macro) 53.2792 WB-Score ▁·▁▁·▃█▄▇▆●▆ beats 44/52 avg 43.019 WB-Score 17% sharp
Open PT-LLM Leaderboard average (… 73.3% ▃▁▁▁▁▁▁▁▂●▅▁ beats 206/276 medium · avg 72% 2.7% noisy
source results share of the score
AlpacaEval 1.0 and 2.0 leaderboards 1 64% ▇▇▇▇▇▇▇▇
WildBench v2 leaderboard data 1 17% ▇▇
LMArena leaderboard dataset 1 16% ▇▇
Open Portuguese LLM Leaderboard 1 3% ▇
prior: pull toward the middle 1% ▇
weighted average = 59.9
Licences: AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · 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/)) · 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 Gemma 2 9B IT SimPO (Q4_K_M, needs ~5.8 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/gemma-2-9b-it-SimPO-GGUF/tree/7278e3d41ac5c1c22145a9e583ff74c13bc2ec77))
```sh
llama cli -hf mradermacher/gemma-2-9b-it-SimPO-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/gemma-2-9b-it-SimPO-GGUF:Q4_K_M
```
Other versions: Q8_0 (~9.8 GB) `llama cli -hf mradermacher/gemma-2-9b-it-SimPO-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 Gemma 2 9B IT SimPO (Q4_K_M, needs ~5.8 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/gemma-2-9b-it-SimPO-GGUF/tree/7278e3d41ac5c1c22145a9e583ff74c13bc2ec77))
```sh
lms get https://huggingface.co/mradermacher/gemma-2-9b-it-SimPO-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 (~9.8 GB) `lms get https://huggingface.co/mradermacher/gemma-2-9b-it-SimPO-GGUF@Q8_0 -y` · 8bit (~9.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/gemma-2-9b-it-SimPO-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 Gemma 2 9B IT SimPO (Q4_K_M, needs ~5.8 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/gemma-2-9b-it-SimPO-GGUF/tree/7278e3d41ac5c1c22145a9e583ff74c13bc2ec77))
```sh
ollama run hf.co/mradermacher/gemma-2-9b-it-SimPO-GGUF:Q4_K_M
```
Other versions: Q8_0 (~9.8 GB) `ollama run hf.co/mradermacher/gemma-2-9b-it-SimPO-GGUF: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 Gemma 2 9B IT SimPO (8bit, needs ~9.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/gemma-2-9b-it-SimPO-8bit/tree/6d20564a1218f86b3b1731a5effb5aff30f0971f))
```sh
mlx_lm.chat --model mlx-community/gemma-2-9b-it-SimPO-8bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/gemma-2-9b-it-SimPO-8bit
```
### Other ways to run it
1. Run it as published (BF16, needs ~18.48 GB, from [huggingface.co/princeton-nlp](https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO/tree/8c87091f412e3aa6f74f66bd86c57fb81cbc3fde))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='princeton-nlp/gemma-2-9b-it-SimPO', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/princeton-nlp](https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO)
[interactive version](https://whatmodel.sh/model/gemma-2-9b-simpo) · [JSON](https://whatmodel.sh/api?format=json&all=1)