whatmodel.sh / FuseChat-Gemma-2-9B-Instruct

FuseChat-Gemma-2-9B-Instruct

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FuseChat-Gemma-2-9B-Instruct is an open-weights language model from FuseAI, released November 2024. Its published size is 9.24B parameters.

Catalogue identity notes

Maker repo FuseAI/FuseChat-Gemma-2-9B-Instruct @0e99419: no licence tag on the card. 9,241,705,984 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-11: month the maker's Hugging Face repo was created (createdAt 2024-11-14).

62.0±1.7#193 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 FuseChat-Gemma-2-9B-Instruct, out of 1132 with a score, then more from FuseAI.

#modelscorewhereRAMreleased
248A.X K1 (519B-A33B) SK Telecom62±1local~1091GB2025-12
249watt-tool-8B Watt AI62±7local~6.1GB2024-12
250FuseChat-Gemma-2-9B-Instruct FuseAI62±2local~7.6GB2024-11
251gpt-oss-120b OpenAI62±1api $0.26/M +local~67GB2025-08
252GPT-5 nano OpenAI62±1api $0.14/M2025-08
more from FuseAI
288FuseChat-Llama-3.1-8B-Instruct FuseAI60±2local~6.6GB2024-11
303FuseChat-Qwen-2.5-7B-Instruct FuseAI59±2local~6.4GB2024-11
398FuseChat-Llama-3.2-3B-Instruct FuseAI56±2local~3.4GB2024-12

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #132 of 466 * · 99% of its score · 1 test

Beats LongCat 2.0 (1.6T-A48B), o3-mini, K-EXAONE (236B-A23B) here, though they rank higher overall. Trails LFM2.5 8B-A1B, LFM2.5 2.6B, Nemotron-Cascade-8B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled70.2% ✎▁▂▂▃▆▆█▆▄▄▁▃ beats 120/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%at its level

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

Other ways to run it

  1. Run it as published · needs ~18.48 GB · from huggingface.co/FuseAI (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='FuseAI/FuseChat-Gemma-2-9B-Instruct', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/FuseAI ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 99%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 62.0

$ 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