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Qwen2 0.5B Instruct

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Qwen2 0.5B Instruct is an open-weights language model from Alibaba, released June 2024. Its published size is 0.49B parameters and a 32k-token context window. Licence: apache-2.0.

Catalogue identity notes

Maker repo Qwen/Qwen2-0.5B-Instruct @c540970: licence tag apache-2.0. 494,032,768 parameters from the Hugging Face safetensors metadata. Dense, so active = total. Released 2024-06: month the maker's Hugging Face repo was created (createdAt 2024-06-03). Ollama tags qwen2:0.5b-instruct-q4_K_M, qwen2:0.5b-instruct-q8_0 are the library's instruct/chat builds of this checkpoint (tag names on the pinned tags page); download sizes are estimated from the parameter count.

25.3±1.7#671 of 719 LLMsfrom 8 benchmark results across 4 sources

$ how it compares

The LLMs ranked either side of Qwen2 0.5B Instruct, out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
774Alpaca 13B Stanford CRFM26±2comparison only2023-03
775Falcon 7B Instruct TII25±2local~5.9GB2023-04
776Qwen2 0.5B Instruct Alibaba25±2local~1.8GB2024-06
777Pythia 12B EleutherAI25±1local~9.5GB2023-02
778RedPajama-INCITE Base 3B v1 Together25±3local~3.4GB2023-05
more from Alibaba
18Qwen3.8 Max (2.4T-A95B) Alibaba78±1api $3.00/M +local~380GB2026-08
34Qwen3.8 Max (0902) Alibaba76±1api $3.00/M2026-09
37Qwen3.8-Flash-Next Alibaba75±1local~59GB2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

coding█··········█ #2 of 2 * · 16% of its score · 1 test
testresultvs the others in its rundifficultycountswhy
HumanEval lab17.1% ✎▁▁▁▁▃▃▅▅█▃▂▁ beats 19/190easy · avg 84%▇▇▇▇▇▇ 16%sharp · small run
chat & writing█··········█ #2 of 2 * · 5.2% of its score · 1 test
testresultvs the others in its rundifficultycountswhy
IFEval prompt-level strict lab20.0% ✎▃▁··▂··▂▇█▃▃ beats 0/26easy · avg 68%▇▇ 5.2%at its level · small run
math▁▃▃▅▆▅▄█▄▁▁▁ #382 of 398 * · 15% of its score · 1 test

Beats Command R (03-2024), Molmo 7B-D (0924), Qwen2-VL 2B Instruct here, though they rank higher overall.

testresultvs the others in its rundifficultycountswhy
GSM8K lab40.1% ✎▂▂▂▅▅▆▅▇█▇▄▃ beats 34/146very easy · avg 91%▇▇▇▇▇▇ 15%sharp · at its level · small run
other tests5 not in a task · 63% of its score
testresultvs the others in its rundifficultycountswhy
LiveBench 2024-07-267.7% ✎▁▃▁▂▂▃▂█▆▃▅▃ beats 2/61medium · avg 41%▇▇▇▇▇▇▇▇▇▇▇▇▇ 32%too hard for it
MMLU lab37.9% ✎▁▁▁▁▁▃▃▅█▃▆▃ beats 17/183easy · avg 80%▇▇▇▇ 10%small run
Open Persian LLM Leaderboard v2 average (21 tasks)30.6% ✎▃▄▂▃█▄▃▆▃▅▂▂ beats 1/81medium · avg 58%▇▇▇▇ 9.2%blunt · at its level
C-Eval lab45.2% ✎▁···▁▁▃▁▂▂▃█ beats 5/33easy · avg 82%▇▇▇ 8.4%small run
Open PT-LLM Leaderboard average (9 Portuguese tasks)44.4% ✎▃▁▁▁▁▁▃▁▂█▅▁ beats 57/276medium · avg 73%▇ 3.2%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 Qwen2 0.5B Instruct · needs ~0.4 GB · from huggingface.co/Qwen (official)
    chat in the terminal
    llama cli -hf Qwen/Qwen2-0.5B-Instruct-GGUF:q4_k_m
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf Qwen/Qwen2-0.5B-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 Qwen2 0.5B Instruct · needs ~0.4 GB · from huggingface.co/Qwen (official)

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/Qwen/Qwen2-0.5B-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 Qwen2 0.5B Instruct · needs ~0.79 GB · from ollama.com
    chat in the terminal
    ollama run qwen2:0.5b-instruct-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 Qwen2 0.5B Instruct · needs ~0.3 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/Qwen2-0.5B-Instruct-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Qwen2-0.5B-Instruct-4bit

Official weights and documentation: huggingface.co/Qwen ↗

$ how the score was worked out · Qwen2-0.5B-Instruct model card 55%, LiveBench model judgments (HF… 32%, Open Persian LLM Leaderboard… 9% 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
Qwen2-0.5B-Instruct model card · lab-reported5▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 55%Apache-2.0
LiveBench model judgments (HF livebench/model_judgment, 2024-10-22 revision) with the LiveBench question datasets1▇▇▇▇▇▇▇▇▇▇ 32%Apache-2.0 (stated in the LiveBench datasheet)
Open Persian LLM Leaderboard v2 (Part DP AI and AUT NLP Lab)1▇▇▇ 9%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇ 3%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 25.3

$ 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.

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