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Code Millenials 34B

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Code Millenials 34B is an open-weights language model from Bud Ecosystem, released January 2024. Its published size is 33.75B parameters. Licence: llama2.

Catalogue identity notes

Maker repo budecosystem/code-millenials-34b @4fcd522: licence tag llama2. 33,745,018,880 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-01: month the maker's Hugging Face repo was created (createdAt 2024-01-02).

48.2±0.9#379 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of Code Millenials 34B, out of 828 with a score.

#modelscorewhereRAMreleased
450XwinCoder-34B Xwin-LM48±1local~23GB2023-11
451StarChat2 15B v0.1 Hugging Face48±1local~12GB2024-03
452Code Millenials 34B Bud Ecosystem48±1local~23GB2024-01
453Reka Core (2024-05-01) Reka AI48±1comparison only2024-05
454Gemini 1.5 Flash-8B (001) Google48±2comparison only2024-10

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #280 of 458 · 100% of its score · 3 tests

Beats Qwen3-VL 32B Instruct, Solar Open 100B (102B-A12B), GLM-4.7-Flash here, though they rank higher overall. Trails DeepSeek LLM 67B Chat, Yi 1.5 9B Chat, Mistral NeMo Instruct (2407) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval74.4% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 68/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 50%sharp
HumanEval+70.7% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 71/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 42%sharp · at its level
MBPP+64.6% ✎▁···▁▁▂▅▇█▆▂ beats 49/75easy · avg 68%▇▇▇ 7.9%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 Millenials 34B · needs ~20.2 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/code-millenials-34b-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/code-millenials-34b-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 Millenials 34B · needs ~20.2 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/code-millenials-34b-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 Millenials 34B · needs ~20.2 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/code-millenials-34b-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~67.49 GB · from huggingface.co/budecosystem (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='budecosystem/code-millenials-34b', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/budecosystem ↗

$ 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 runs3▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 100%Apache-2.0
prior · pull toward the middle▇ 0%
weighted average= 48.2

$ 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