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Qwen2 1.5B Instruct

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Qwen2 1.5B Instruct is an open-weights language model from Alibaba, released June 2024. Its published size is 1.54B parameters and a 32k-token context window. Licence: apache-2.0.

Catalogue identity notes

Maker repo Qwen/Qwen2-1.5B-Instruct @ba1cf18: licence tag apache-2.0. 1,543,714,304 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 qwen2:1.5b-instruct-q4_K_M, qwen2:1.5b-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.

31.1±0.8#634 of 719 LLMsfrom 14 benchmark results across 7 sources

$ how it compares

The LLMs ranked either side of Qwen2 1.5B Instruct, out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
729Falcon-H1 1.5B Instruct TII31±4local~2.4GB2025-05
730Flan-T5 XL Google31±2local~14GB2022-10
731Qwen2 1.5B Instruct Alibaba31±1local~2.4GB2024-06
732Qwen-7B-Chat Alibaba31±1local~6.6GB2023-08
733Gemma 3 1B Google31±1local~2.2GB2025-03
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

agent tasks▁▃▃▆█▆▅▅▅▄▃▁ #295 of 300 * · 1.3% of its score · 1 test

Beats QwQ-32B-Preview, DeepSeek-Coder-V2 Lite Instruct (16B-A2.4B) here, though they rank higher overall. Trails Granite 4.0 350M, Qwen2.5 0.5B Instruct, Granite 4.0 H 350M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
BFCL v328.2% ✎▁▁▃▁▁▃▅█▅▄▁▁ beats 7/114medium · avg 52%▇ 1.3%blunt · at its level
coding█··········█ #1 of 2 * · 4.6% of its score · 1 test
testresultvs the others in its rundifficultycountswhy
HumanEval lab37.8% ✎▁▁▁▁▁▃▅▅█▃▂▁ beats 48/190easy · avg 84%▇▇ 4.6%sharp · at its level · small run
chat & writing█··········█ #1 of 2 * · 1.4% of its score · 1 test
testresultvs the others in its rundifficultycountswhy
IFEval prompt-level strict lab29.0% ✎▁▃··▂··▂▇█▃▃ beats 1/26easy · avg 68%▇ 1.4%at its level · small run
math▁▃▃▅▆▅▄█▄▁▁▁ #389 of 398 · 43% of its score · 4 tests

Beats Code Llama 7B (base), Llama 2 13B Chat, DeepSeek-Coder 1.3B Base here, though they rank higher overall. Trails Qwen2 0.5B Instruct, Granite 3.1 1B-A400M Instruct, Gemma 2B (base) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
GSM8K lab +164.1% ✎▄···█▄▄·▄·▄█ beats 0/8very easy · avg 92%▇▇▇▇▇▇▇ 18%sharp · at its level · several runs
GSM8K (ZeroEval, zero-shot)43.4% ✎▃▄·▁▄█▅▅▅███ beats 0/43very easy · avg 89%▇▇▇▇▇▇▇ 18%sharp · at its level
MATH (subset unspecified) lab25.1% ✎▃····▃·▅·▅·█ beats 0/8easy · avg 76%▇▇ 4.4%at its level
Minerva MATH lab5.5% ✎▃·····▃··█▅▅ beats 0/8medium · avg 37%▇ 2.7%typical
other tests6 not in a task · 49% of its score
testresultvs the others in its rundifficultycountswhy
MMLU-Redux (ZeroEval, 2,778 questions)41.1% ✎▃·▁▁▄▄▄▅█▄▂▁ beats 0/53easy · avg 77%▇▇▇▇▇▇▇▇▇▇ 26%at its level
LiveBench 2024-07-2610.3% ✎▁▁▃▂▂▃▂█▆▃▅▃ beats 4/61medium · avg 41%▇▇▇▇▇ 12%typical
MMLU lab52.4% ✎▁▁▁▁▁▂▃▅█▃▆▃ beats 43/183easy · avg 80%▇ 3.0%at its level · small run
Polish EQ-Bench v2 (SpeakLeash)15.3% ✎▁··▃▁▂▂▆▅▆█▆ beats 1/70medium · avg 64%▇ 2.9%at its level
Open Persian LLM Leaderboard v2 average (21 tasks)36.8% ✎▂▄▂▃█▄▃▆▃▅▂▂ beats 10/81medium · avg 58%▇ 2.6%blunt · at its level
C-Eval lab63.8% ✎▁···▁▁▂▁▃▂▃█ beats 9/33easy · avg 82%▇ 2.4%at its level · small run

$ 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 1.5B Instruct · needs ~1 GB · from huggingface.co/Qwen (official)
    chat in the terminal
    llama cli -hf Qwen/Qwen2-1.5B-Instruct-GGUF:q4_k_m
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf Qwen/Qwen2-1.5B-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 1.5B Instruct · needs ~1 GB · from huggingface.co/Qwen (official)

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/Qwen/Qwen2-1.5B-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 1.5B Instruct · needs ~1.42 GB · from ollama.com
    chat in the terminal
    ollama run qwen2:1.5b-instruct-q4_K_M

MLX (Mac)

version

  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 1.5B Instruct · needs ~0.9 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/Qwen2-1.5B-Instruct-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Qwen2-1.5B-Instruct-4bit

Official weights and documentation: huggingface.co/Qwen ↗

$ how the score was worked out · WildEval ZeroEval published r… 44%, Qwen (Alibaba) vendor-reporte… 22%, Qwen2-0.5B-Instruct model card 15% and 4 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
WildEval ZeroEval published results2▇▇▇▇▇▇▇▇▇▇▇▇▇ 44%Apache-2.0
Qwen (Alibaba) vendor-reported benchmark scores for its own models (qwen.ai launch posts, Qwen2.5-VL and Qwen3-VL technical reports, Qwen3.8-Flash-Next card) · lab-reported3▇▇▇▇▇▇ 22%Factual vendor-reported score (not licensed content)
Qwen2-0.5B-Instruct model card · lab-reported5▇▇▇▇▇ 15%Apache-2.0
LiveBench model judgments (HF livebench/model_judgment, 2024-10-22 revision) with the LiveBench question datasets1▇▇▇▇ 12%Apache-2.0 (stated in the LiveBench datasheet)
SpeakLeash Polish EQ-Bench leaderboard1▇ 3%Apache-2.0
Open Persian LLM Leaderboard v2 (Part DP AI and AUT NLP Lab)1▇ 3%Apache-2.0
Berkeley Function Calling Leaderboard result archive1▇ 1%Apache-2.0
prior · pull toward the middle▇ 0%
weighted average= 31.1

$ improve this page

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