whatmodel.sh / Gemma 2 9B IT SimPO

Gemma 2 9B IT SimPO

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Gemma 2 9B IT SimPO is an open-weights language model from Princeton NLP, released July 2024. Its published size is 9.24B parameters. Licence: mit.

Catalogue identity notes

Comparison-only. Arena entry 'gemma-2-9b-it-simpo' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'gemma-2-9b-it-simpo' as 'Gemma-2-9B-it-SimPO' (Princeton, MIT), linking https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO. Maker repo princeton-nlp/gemma-2-9b-it-SimPO @8c87091: licence tag mit. 9,241,705,984 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-07: month the maker's Hugging Face repo was created (createdAt 2024-07-16).

59.2±1.3#205 of 719 LLMsfrom 4 benchmark results across 4 sources

$ how it compares

The LLMs ranked either side of Gemma 2 9B IT SimPO, out of 828 with a score, then more from Princeton NLP.

#modelscorewhereRAMreleased
245OpenReasoning-Nemotron-32B NVIDIA59±2local~21GB2025-07
246DeepSeek V3 (Dec '24) DeepSeek59±1api $0.46/M +local~337GB2024-12
247Gemma 2 9B IT SimPO Princeton NLP59±1local~7.6GB2024-07
248Gemma 2 9B IT DPO (Princeton) Princeton NLP59±1local~7.6GB2024-07
249Trinity-Large-Preview Arcee AI59±2local~155GB2026-01
more from Princeton NLP
400Llama-3-Instruct-8B-SimPO Princeton NLP51±1local~6.6GB2024-05
408Llama-3-8B-ProLong-512k-Instruct Princeton NLP50±5local~35GB2024-08
647Llama-3-Instruct-8B-SimPO-v0.2 Princeton NLP39±1local~6.6GB2024-07

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #216 of 393 * · 79% of its score · 2 tests

Beats Granite 4.2 8B, Mistral Small 4 (119B-A6.5B) here, though they rank higher overall. Trails LFM2.5 1.2B Instruct, Granite 4.0 H Micro 3B, Gemma 3n E4B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled72.4% ✎▁▂▂▃▆▆█▆▄▄▁▃ beats 121/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 64%at its level
LMArena (text)1280.3 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 111/379avg 1268▇▇▇▇▇▇ 15%blunt
other tests2 not in a task · 20% of its score
testresultvs the others in its rundifficultycountswhy
WildBench v2 WB-Score (task macro)53.2792 WB-Score ✎▁·▁▁·▃█▄▇▆▅▆ beats 44/52avg 45.199 WB-Score▇▇▇▇▇▇▇ 17%sharp
Open PT-LLM Leaderboard average (9 Portuguese tasks)73.3% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 206/276medium · avg 73%▇ 2.5%noisy

$ where to run it

llama.cpp · recommended

version

  1. 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 | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Gemma 2 9B IT SimPO · needs ~5.8 GB · from huggingface.co/mradermacher
    chat in the terminal
    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)
    llama serve -hf mradermacher/gemma-2-9b-it-SimPO-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Gemma 2 9B IT SimPO · needs ~5.8 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/gemma-2-9b-it-SimPO-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Gemma 2 9B IT SimPO · needs ~5.8 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/gemma-2-9b-it-SimPO-GGUF:Q4_K_M

MLX (Mac)

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Gemma 2 9B IT SimPO · needs ~9.8 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/gemma-2-9b-it-SimPO-8bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/gemma-2-9b-it-SimPO-8bit

Other ways to run it

  1. Run it as published · needs ~18.48 GB · from huggingface.co/princeton-nlp (official)
    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'])"

Official weights and documentation: huggingface.co/princeton-nlp ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 64%, WildBench v2 leaderboard data… 17%, LMArena leaderboard dataset 15% and 1 more

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

sourceresultsshare of the scorelicence
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 64%Apache-2.0
WildBench v2 leaderboard data (Ai2)1▇▇▇▇▇ 17%Apache-2.0
LMArena leaderboard dataset1▇▇▇▇▇ 15%CC BY 4.0
Open Portuguese LLM Leaderboard (archived)1▇ 3%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 59.2

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

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