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Tulu 2 13B

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Tulu 2 13B is an open-weights language model from Allen Institute for AI, released November 2023. Its published size is 13B parameters. Licence: AI2 ImpACT Low-risk; Llama 2 base terms.

Catalogue identity notes

Exact full-precision model evaluated in the Tulu 2 paper (arXiv:2311.10702v2). QLoRA ablations are separate and not imported.

Model information sources

Release date ↗ · Weights licence ↗

38.2±1.7#561 of 719 LLMsfrom 6 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Tulu 2 13B, out of 828 with a score, then more from Allen Institute for AI.

#modelscorewhereRAMreleased
651CodeGemma 7B (v1.0) Google38±1local~7.1GB2024-03
652Code Llama 13B Python Meta38±1local~9.8GB2023-08
653Tulu 2 13B Allen Institute for AI38±2local~9.8GB2023-11
654Phi-3-mini-4k-instruct (Apr 2024) Microsoft38±2local~4GB2024-04
655Apertus 8B Instruct (2509) Swiss AI38±4local~6.3GB2025-08
more from Allen Institute for AI
493OLMo 2 13B Instruct (1124) Allen Institute for AI46±1local~9.6GB2024-12
516Tulu 2 70B Allen Institute for AI46±2local~28GB2023-11
557Code Tulu 2 34B Allen Institute for AI44±2local~72GB2023-11

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #430 of 458 * · 21% of its score · 1 test

Beats Qwen3.5 2B, Molmo 7B-D (0924) here, though they rank higher overall. Trails Llama 3.2 1B Instruct, SmolLM2 1.7B Instruct, Mistral 7B Instruct v0.1 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Tulu 2: CodexEval pass@10 lab49.0% ✎▃▃▃·▆▃▃█▃▂▂▅ beats 7/25very easy · avg 86%▇▇▇▇▇▇▇▇ 21%sharp · at its level
math▁▂▃▅▆▅▄█▄▁▁▁ #323 of 398 * · 14% of its score · 1 test

Beats Athene-70B, GPT-3.5 Turbo (0125), Qwen2-VL 7B Instruct here, though they rank higher overall. Trails Gemma 7B (base), DeepSeek R1 Distill Qwen 1.5B, Tulu 2 DPO 13B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
GSM8K lab46.0% ✎▆▂▅█▅▅▃▆···▂ beats 15/25easy · avg 73%▇▇▇▇▇ 14%sharp · at its level
other tests4 not in a task · 64% of its score
testresultvs the others in its rundifficultycountswhy
BIG-Bench Hard lab49.5% ✎▁·····▂█▂▅·▁ beats 14/25easy · avg 77%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 40%sharp · at its level
MMLU lab55.4% ✎▁▁▃▃▄█▄▁█··▁ beats 16/25easy · avg 74%▇▇▇▇▇▇ 14%at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)63.5% ✎▃▁▁▁▁▁▁▁▃█▅▁ beats 103/276medium · avg 73%▇▇ 5.4%at its level · noisy
Tulu 2: TyDiQA gold passage, 1-shot F1 lab53.2% ✎▂··▁▃▂▂▂█▅▁▂ beats 17/25medium · avg 61%▇▇ 4.7%blunt · 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 Tulu 2 13B · needs ~7.9 GB · from huggingface.co/TheBloke
    chat in the terminal
    llama cli -hf TheBloke/tulu-2-13B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf TheBloke/tulu-2-13B-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 Tulu 2 13B · needs ~7.9 GB · from huggingface.co/TheBloke

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/TheBloke/tulu-2-13B-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 Tulu 2 13B · needs ~7.9 GB · from huggingface.co/TheBloke
    chat in the terminal
    ollama run hf.co/TheBloke/tulu-2-13B-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~26.03 GB · from huggingface.co/allenai
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='allenai/tulu-2-13b', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Weights and documentation: huggingface.co/allenai ↗

$ how the score was worked out · Tulu 2 — base and instruction… 93%, Open Portuguese LLM Leaderboa… 5%

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
Tulu 2 — base and instruction-tuned evaluations · lab-reported5▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%CC BY 4.0
Open Portuguese LLM Leaderboard (archived)1▇▇ 5%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 38.2

$ improve this page

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data updated · sources and licences · json