[whatmodel.sh](../../README.md) / StarCoder2-15B-Instruct-v0.1
# StarCoder2-15B-Instruct-v0.1
BigCode · released 2024-04 · 15.96B params · bigcode-openrail-m
**score 47.0** ±0.8 · #438 of 732 · from 5 benchmark results across 2 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks StarCoder2-15B-Instruct-v0.1 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #331 of 469 86% 3
coding · #331 of 469 · 86% of its score
Beats [Qwen3-VL 8B Instruct](qwen3-vl-8b-instruct.md), [Qwen3-VL 4B Instruct](qwen3-vl-4b-instruct.md), [LFM2.5 2.6B](lfm2-5-2-6b.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Llama 3.2 11B Vision Instruct](llama-3-2-11b-vision.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 67.7% ▁·▁▁▃▄▆●█▃▁▁ beats 53/99 easy · avg 76% 42% sharp · at its level
HumanEval+ 60.4% ▁·▁▁▁▃▄▄●█▃▁ beats 52/99 easy · avg 70% 36% sharp · at its level
MBPP+ 65.1% ▁···▁▁▂▅▇●▆▂ beats 52/75 easy · avg 66% 7.1% at its level
other tests (2)
test result vs the others in its run difficulty counts why
BigCodeBench (Instruct) (maker) 36.7% ▃▂·▆▆█●▆▅▃▃▃ beats 20/33 medium · avg 39% 7.1% at its level
BigCodeBench (Complete) (maker) 43.7% ▂▁▁▄▂▅▇●▆▂▂▂ beats 38/58 medium · avg 48% 7.0% at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 86% ▇▇▇▇▇▇▇▇▇▇
BigCodeBench paper (submitted / maker-r… 2 14% ▇▇
prior: pull toward the middle 0% ▇
weighted average = 47.0
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/))
## 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 StarCoder2-15B-Instruct-v0.1 (Q4_K_M, needs ~9.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/tree/19f8e47b8a6b93759dda71d225643adf94847a60))
```sh
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)
```sh
llama serve -hf mradermacher/starcoder2-15b-instruct-v0.1-GGUF:Q4_K_M
```
Other versions: Q8_0 (~17 GB) `llama cli -hf mradermacher/starcoder2-15b-instruct-v0.1-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 StarCoder2-15B-Instruct-v0.1 (Q4_K_M, needs ~9.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-GGUF/tree/19f8e47b8a6b93759dda71d225643adf94847a60))
```sh
lms get https://huggingface.co/mradermacher/starcoder2-15b-instruct-v0.1-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/starcoder2-15b-instruct-v0.1-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 StarCoder2-15B-Instruct-v0.1 (Q4_0, needs ~9.1 GB, from [ollama.com](https://ollama.com/library/starcoder2/tags))
```sh
ollama run starcoder2:15b-instruct
```
Other versions: Q4_0 (~9.1 GB) `ollama run starcoder2:15b-instruct`
### Other ways to run it
1. Run it as published (BF16, needs ~31.92 GB, from [huggingface.co/bigcode](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1/tree/ffb8dd9776ba9a66d655ecd962e882f3013e9f7c))
```sh
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'])"
```
Model repository and documentation: [huggingface.co/bigcode](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1)
[interactive version](https://whatmodel.sh/model/starcoder2-15b-instruct-v0-1) · [JSON](https://whatmodel.sh/api?format=json&all=1)