[whatmodel.sh](../../README.md) / StarCoder 15.5B
# StarCoder 15.5B
BigCode · released 2023-04 · 15.82B params · bigcode-openrail-m
**score 36.4** ±0.7 · #616 of 732 · from 7 benchmark results across 4 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks StarCoder 15.5B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁●▅▇█▇▅▄▃▁ #423 of 469 89% 3
coding · #423 of 469 · 89% of its score
Beats [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [DeepSeek R1 Distill Llama 8B](deepseek-r1-distill-llama-8b.md) here, though they rank higher overall. Trails [Llama 3.2 1B Instruct](llama-3-2-1b.md), [SmolLM2 1.7B Instruct](smollm2-1-7b.md), [Granite 3B Code Base (2K)](granite-3b-code-base-2k.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 34.1% ▁·▁▁▃●▆▅█▃▁▁ beats 19/99 easy · avg 76% 58% sharp · at its level · several runs
HumanEval+ 29.3% ▁·▁▁▁▃●▄▄█▃▁ beats 19/99 easy · avg 70% 26% sharp · at its level
MBPP+ 46.1% ▁···▁▁▂●▇█▆▂ beats 13/75 easy · avg 66% 5.7% at its level
other tests (2)
test result vs the others in its run difficulty counts why
EvoEval (overall) 23.5% ▂▂▃●▄█▁▄▅▃▁▃ beats 8/42 medium · avg 52% 10% typical
ODEX English pass@1 (zero-shot) 44.7% ▄▄········●█ beats 2/4 medium · avg 58% 0.8% blunt · at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 67% ▇▇▇▇▇▇▇▇
Top Leaderboard Ranking = Top Coding Pr… 2 30% ▇▇▇▇
StarCoder: may the source be with you! … 1 2% ▇
Execution-Based Evaluation for Open-Dom… 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 36.4
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · Top Leaderboard Ranking = Top Coding Proficiency ([CC BY 4.0](../../licenses/paper-2403.19114v1-CC-BY-4.0.txt)) · StarCoder: may the source be with you! ([CC BY-SA 4.0](../../licenses/paper-2305.06161v2-starcoder-CC-BY-SA-4-0.txt)) · Execution-Based Evaluation for Open-Domain Code Generation ([CC BY 4.0](../../licenses/acl-2023-findings-emnlp-89-CC-BY-4-0.txt))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run StarCoder 15.5B (Q4_K_M, needs ~10.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/starcoder-GGUF/tree/081cdab8112887d9e332e652551f67e50f33ca55))
```sh
llama cli -hf mradermacher/starcoder-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/starcoder-GGUF:Q4_K_M
```
Other versions: Q8_0 (~17 GB) `llama cli -hf mradermacher/starcoder-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run StarCoder 15.5B (Q4_K_M, needs ~10.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/starcoder-GGUF/tree/081cdab8112887d9e332e652551f67e50f33ca55))
```sh
lms get https://huggingface.co/mradermacher/starcoder-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~17 GB) `lms get https://huggingface.co/mradermacher/starcoder-GGUF@Q8_0 -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run StarCoder 15.5B (Q4_0, needs ~9 GB, from [ollama.com](https://ollama.com/library/starcoder/tags))
```sh
ollama run starcoder:15b
```
Other versions: Q4_0 (~9 GB) `ollama run starcoder:15b`
### Other ways to run it
1. Run it as published (fp32, needs ~63.28 GB, from [huggingface.co/bigcode](https://huggingface.co/bigcode/starcoder/tree/827cd7721231872c152e3faef5cae1b22964ed58))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='bigcode/starcoder', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/bigcode](https://huggingface.co/bigcode/starcoder)
[interactive version](https://whatmodel.sh/model/starcoder) · [JSON](https://whatmodel.sh/api?format=json&all=1)