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XwinCoder-34B

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XwinCoder-34B is an open-weights language model from Xwin-LM, released November 2023. Licence: llama2.

Catalogue identity notes

Maker repo Xwin-LM/XwinCoder-34B @c331e0f: licence tag llama2. Released 2023-11: month the maker's Hugging Face repo was created (createdAt 2023-11-13).

48.3±0.9#377 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of XwinCoder-34B, out of 828 with a score, then more from Xwin-LM.

#modelscorewhereRAMreleased
448Qwen2.5 7B Instruct Alibaba48±1api $0.30/M +local~6GB2024-09
449DeepSeek R1 Distill Qwen 7B DeepSeek48±1local~6GB2025-01
450XwinCoder-34B Xwin-LM48±1local~23GB2023-11
451StarChat2 15B v0.1 Hugging Face48±1local~12GB2024-03
452Code Millenials 34B Bud Ecosystem48±1local~23GB2024-01
more from Xwin-LM
552Xwin-LM 70B v0.1 Xwin-LM44±1local~28GB2023-09

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #278 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
HumanEval75.6% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 72/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 50%sharp
HumanEval+69.5% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 68/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 42%sharp · at its level
MBPP+64.8% ✎▁···▁▁▂▅▇█▆▂ beats 51/75easy · 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 XwinCoder-34B · needs ~20.2 GB · from huggingface.co/TheBloke
    chat in the terminal
    llama cli -hf TheBloke/XwinCoder-34B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf TheBloke/XwinCoder-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 XwinCoder-34B · needs ~20.2 GB · from huggingface.co/TheBloke

    open in LM Studio ↗

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

Other ways to run it

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

Official weights and documentation: huggingface.co/Xwin-LM ↗

$ 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= 48.3

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