whatmodel.sh / StarCoder2-15B-Instruct-v0.1

StarCoder2-15B-Instruct-v0.1

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StarCoder2-15B-Instruct-v0.1 is an open-weights language model from BigCode, released April 2024. Its published size is 15.96B parameters and a 16k-token context window. Licence: bigcode-openrail-m.

Catalogue identity notes

Maker repo bigcode/starcoder2-15b-instruct-v0.1 @ffb8dd9: licence tag bigcode-openrail-m. 15,957,889,024 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-04: month the maker's Hugging Face repo was created (createdAt 2024-04-23).

45.5±0.8#436 of 719 LLMsfrom 5 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of StarCoder2-15B-Instruct-v0.1, out of 828 with a score, then more from BigCode.

#modelscorewhereRAMreleased
519Olmo 3 7B Instruct Ai246±1local~5.8GB2025-11
520InternLM2.5 20B Chat Shanghai AI Laboratory (InternLM)46±2local~14GB2024-08
521StarCoder2-15B-Instruct-v0.1 BigCode46±1local~11GB2024-04
522Qwen1.5 72B Chat Alibaba45±1local~31GB2024-02
523speechless-starcoder2-15b uukuguy45±1local~12GB2024-03
more from BigCode
639StarCoder2 15B BigCode39±1local~11GB2024-02
698StarCoder2 7B BigCode35±1local~6.1GB2024-02
701StarCoder 15.5B BigCode35±1local~11GB2023-04

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #326 of 458 · 87% of its score · 3 tests

Beats GLM-4.7-Flash, Qwen3-VL 4B Instruct, LFM2.5 2.6B here, though they rank higher overall. Trails Phi-3-mini-128k-instruct (Apr 2024), DeepSeek LLM 67B Chat, WizardLM 70B V1.0 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval67.7% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 53/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 45%sharp · at its level
HumanEval+60.4% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 52/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 35%sharp · at its level
MBPP+65.1% ✎▁···▁▁▂▅▇█▆▂ beats 52/75easy · avg 68%▇▇▇ 6.4%at its level
other tests2 not in a task · 13% of its score
testresultvs the others in its rundifficultycountswhy
BigCodeBench (Complete) lab43.7% ✎▂▁▁▄▂▅▇█▆▂▂▂ beats 38/58medium · avg 50%▇▇▇ 6.4%at its level
BigCodeBench (Instruct) lab36.7% ✎▃▂·▆▆██▆▅▃▃▃ beats 20/33medium · avg 41%▇▇▇ 6.4%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 StarCoder2-15B-Instruct-v0.1 · needs ~9.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/starcoder2-15b-instruct-v0.1-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/starcoder2-15b-instruct-v0.1-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 StarCoder2-15B-Instruct-v0.1 · needs ~9.9 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-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 StarCoder2-15B-Instruct-v0.1 · needs ~9.1 GB · from ollama.com
    chat in the terminal
    ollama run starcoder2:15b-instruct

Other ways to run it

  1. Run it as published · needs ~31.92 GB · from huggingface.co/bigcode (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='bigcode/starcoder2-15b-instruct-v0.1', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/bigcode ↗

$ how the score was worked out · EvalPlus / EvalPerf published… 87%, BigCodeBench paper (Zhuo et a… 13%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 87%Apache-2.0
BigCodeBench paper (Zhuo et al., ICLR 2025), arXiv 2406.15877v4 · lab-reported2▇▇▇▇ 13%CC BY 4.0
prior · pull toward the middle▇ 0%
weighted average= 45.5

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

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