whatmodel.sh / gemma-2-9b-it-WPO-HB

gemma-2-9b-it-WPO-HB

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gemma-2-9b-it-WPO-HB is an open-weights language model from wzhouad (WPO), released August 2024. Its published size is 9.24B parameters.

Catalogue identity notes

Maker repo wzhouad/gemma-2-9b-it-WPO-HB @5934cb2: no licence tag on the card. 9,241,705,984 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-08: month the maker's Hugging Face repo was created (createdAt 2024-08-08).

65.0±1.8#154 of 949 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of gemma-2-9b-it-WPO-HB, out of 1132 with a score, then more from wzhouad (WPO).

#modelscorewhereRAMreleased
191DeepSeek R1 0528 DeepSeek65±1api $0.91/M +local~110GB2025-05
192EXAONE 4.5 33B LG AI Research65±2local~24GB2026-04
193gemma-2-9b-it-WPO-HB wzhouad (WPO)65±2local~7.6GB2024-08
194Grok 4.1 SpaceXAI (xAI)65±2comparison only2025-11
195Qwen3.5 Flash Alibaba65±1api $0.17/M2026-02
more from wzhouad (WPO)
401Llama3-Instruct-8B-WPO-HB-v2 wzhouad (WPO)56±2local~35GB2024-07

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #104 of 466 * · 98% of its score · 1 test

Beats LongCat 2.0 (1.6T-A48B), Inkling Small (266B-A12B), GPT-5.4 nano here, though they rank higher overall. Trails Mistral Large 3 (675B-A41B), GPT-4.1, Gemma 4 12B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled76.7% ✎▁▂▂▃▆▆█▆▄▄▁▃ beats 122/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%typical

$ 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-WPO-HB · needs ~5.8 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/gemma-2-9b-it-WPO-HB-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf bartowski/gemma-2-9b-it-WPO-HB-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-WPO-HB · needs ~5.8 GB · from huggingface.co/bartowski

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/bartowski/gemma-2-9b-it-WPO-HB-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-WPO-HB · needs ~5.8 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/gemma-2-9b-it-WPO-HB-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~36.97 GB · from huggingface.co/wzhouad (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='wzhouad/gemma-2-9b-it-WPO-HB', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/wzhouad ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 98%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%Apache-2.0
prior · pull toward the middle▇ 2%
weighted average= 65.0

$ 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