[whatmodel.sh](../../README.md) / Gemma 2 9B IT DPO (Princeton)
# Gemma 2 9B IT DPO (Princeton)
Princeton NLP · released 2024-07 · 9.24B params
**score 59.9** ±1.3 · #204 of 732 · from 3 benchmark results across 3 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 DPO (Princeton) among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆▄▆█●▇▇▃ #142 of 402* 76% 1
chat & writing · #142 of 402 · 76% of its score
Beats [o3-mini](o3-mini.md), [INTELLECT-3](intellect-3.md), [MiniMax-M2](minimax-m2.md) here, though they rank higher overall. Trails [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [LFM2.5 2.6B](lfm2-5-2-6b.md), [Spark-X2.5 1.7B](spark-x2-5-1-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 67.7% ▁▂▂▃▆▆█▆▄▄●▁ beats 119/122 medium · avg 35% 76% sharp · at its level
other tests (2)
test result vs the others in its run difficulty counts why
WildBench v2 WB-Score (task macro) 53.2230 WB-Score ▁·▁▁·▃█▄▇▆●▆ beats 43/52 avg 43.019 WB-Score 20% sharp
Open PT-LLM Leaderboard average (… 70.4% ▃▁▁▁▁▁▁▁▂●▅▁ beats 167/276 medium · avg 72% 3.2% noisy
source results share of the score
AlpacaEval 1.0 and 2.0 leaderboards 1 76% ▇▇▇▇▇▇▇▇▇
WildBench v2 leaderboard data 1 20% ▇▇
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)) · 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 DPO (Princeton) (Q4_K_M, needs ~5.8 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/gemma-2-9b-it-DPO-GGUF/tree/f839c0b279905194bf3c896eb6fb6f79d9a64c4b))
```sh
llama cli -hf mradermacher/gemma-2-9b-it-DPO-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-DPO-GGUF:Q4_K_M
```
Other versions: Q8_0 (~9.8 GB) `llama cli -hf mradermacher/gemma-2-9b-it-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 Gemma 2 9B IT DPO (Princeton) (Q4_K_M, needs ~5.8 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/gemma-2-9b-it-DPO-GGUF/tree/f839c0b279905194bf3c896eb6fb6f79d9a64c4b))
```sh
lms get https://huggingface.co/mradermacher/gemma-2-9b-it-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 (~9.8 GB) `lms get https://huggingface.co/mradermacher/gemma-2-9b-it-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 Gemma 2 9B IT DPO (Princeton) (Q4_K_M, needs ~5.8 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/gemma-2-9b-it-DPO-GGUF/tree/f839c0b279905194bf3c896eb6fb6f79d9a64c4b))
```sh
ollama run hf.co/mradermacher/gemma-2-9b-it-DPO-GGUF:Q4_K_M
```
Other versions: Q8_0 (~9.8 GB) `ollama run hf.co/mradermacher/gemma-2-9b-it-DPO-GGUF:Q8_0`
### 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-DPO/tree/f646c99fc3aa7afc7b22c3c7115fd03a40fc1d22))
```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-DPO', 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-DPO)
[interactive version](https://whatmodel.sh/model/gemma-2-9b-it-dpo) · [JSON](https://whatmodel.sh/api?format=json&all=1)