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Code-33B (ajibawa-2023)

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Code-33B (ajibawa-2023) is an open-weights language model from ajibawa-2023, released December 2023. Licence: cc-by-nc-nd-4.0.

Catalogue identity notes

Maker repo ajibawa-2023/Code-33B @cec263d: licence tag cc-by-nc-nd-4.0. Released 2023-12: month the maker's Hugging Face repo was created (createdAt 2023-12-12).

41.0±0.8#691 of 949 LLMsfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Code-33B (ajibawa-2023), out of 1132 with a score, then more from ajibawa-2023.

#modelscorewhereRAMreleased
850Qwen1.5 14B Chat Alibaba41±1local~11GB2024-02
851Code Llama 34B Python Meta41±1local~23GB2023-08
852Code-33B (ajibawa-2023) ajibawa-202341±1local~22GB2023-12
853speechless-coding-7b-16k-tora uukuguy41±1local~16GB2023-10
854DeepSeek-Coder 33B Base DeepSeek41±1local~22GB2023-10
more from ajibawa-2023
753Code-290k-6.7B-Instruct ajibawa-202344±1local~5.8GB2024-02
836Code-13B (ajibawa-2023) ajibawa-202342±1local~9.8GB2023-12
866OpenHermes-2.5-Code-290k-13B ajibawa-202341±1local~9.8GB2024-03

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #366 of 469 * · 100% of its score · 2 tests

Beats LFM2.5 8B-A1B, Qwen3-VL 2B Thinking, DeepSeek R1 Distill Qwen 7B here, though they rank higher overall. Trails Qwen2.5 1.5B Instruct, Phi-3-mini-128k-instruct (Apr 2024), Code Tulu 2 13B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval54.9% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 41/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 58%sharp · at its level
HumanEval+49.4% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 40/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 42%sharp · 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 Code-33B (ajibawa-2023) · needs ~19.6 GB · from huggingface.co/TheBloke
    chat in the terminal
    llama cli -hf TheBloke/Code-33B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf TheBloke/Code-33B-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 Code-33B (ajibawa-2023) · needs ~19.6 GB · from huggingface.co/TheBloke

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/TheBloke/Code-33B-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 Code-33B (ajibawa-2023) · needs ~19.6 GB · from huggingface.co/TheBloke
    chat in the terminal
    ollama run hf.co/TheBloke/Code-33B-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~65.06 GB · from huggingface.co/ajibawa-2023 (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='ajibawa-2023/Code-33B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/ajibawa-2023 ↗

$ how the score was worked out · EvalPlus / EvalPerf published… 100%

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
EvalPlus / EvalPerf published evaluation runs2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 100%Apache-2.0
prior · pull toward the middle▇ 0%
weighted average= 41.0

$ improve this page

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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