whatmodel.sh / FuseChat-Llama-3.2-3B-Instruct

FuseChat-Llama-3.2-3B-Instruct

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FuseChat-Llama-3.2-3B-Instruct is an open-weights language model from FuseAI, released December 2024. Its published size is 3.21B parameters.

Catalogue identity notes

Maker repo FuseAI/FuseChat-Llama-3.2-3B-Instruct @db20845: no licence tag on the card. 3,212,749,824 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-12: month the maker's Hugging Face repo was created (createdAt 2024-12-06).

55.7±1.6#300 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-Llama-3.2-3B-Instruct, out of 1132 with a score, then more from FuseAI.

#modelscorewhereRAMreleased
396Hunyuan-Large-Vision Tencent56±3comparison only2025-05
397K2-Think MBZUAI IFM (LLM360)56±4local~21GB2025-09
398FuseChat-Llama-3.2-3B-Instruct FuseAI56±2local~3.4GB2024-12
399Yehia 7B Reasoning (preview) JasperV1356±4local~16GB2025-04
400Magistral Medium 1.0 (2506) Mistral56±1comparison only2025-06
more from FuseAI
250FuseChat-Gemma-2-9B-Instruct FuseAI62±2local~7.6GB2024-11
288FuseChat-Llama-3.1-8B-Instruct FuseAI60±2local~6.6GB2024-11
303FuseChat-Qwen-2.5-7B-Instruct FuseAI59±2local~6.4GB2024-11

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #207 of 466 * · 99% of its score · 1 test

Beats Granite 4.2 8B, Qwen3-VL 30B-A3B Thinking, Mistral Small 4 (119B-A6.5B) here, though they rank higher overall. Trails LFM2.5 1.2B Instruct, LFM2.5 1.2B Thinking, Gemma 3 12B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled54.0% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 113/122medium · 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-Llama-3.2-3B-Instruct · needs ~2 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/FuseChat-Llama-3.2-3B-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-Llama-3.2-3B-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-Llama-3.2-3B-Instruct · needs ~2 GB · from huggingface.co/bartowski

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/bartowski/FuseChat-Llama-3.2-3B-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-Llama-3.2-3B-Instruct · needs ~2 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/FuseChat-Llama-3.2-3B-Instruct-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 FuseChat-Llama-3.2-3B-Instruct · needs ~1.8 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/FuseChat-Llama-3.2-3B-Instruct-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/FuseChat-Llama-3.2-3B-Instruct-4bit

Other ways to run it

  1. Run it as published · needs ~6.43 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-Llama-3.2-3B-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= 55.7

$ 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