[whatmodel.sh](../../README.md) / StarCoderBase 15.5B
# StarCoderBase 15.5B
BigCode · released 2023-05 · 15.5B params · bigcode-openrail-m
**score 35.1** ±0.9 · #626 of 732 · from 14 benchmark results across 5 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks StarCoderBase 15.5B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁●▅▇█▇▅▄▃▁ #428 of 469 91% 8
math ▁▁▃●█▆▄█▄▂▁▁ #372 of 431* 7.0% 1
coding · #428 of 469 · 91% 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 (maker) 29.3% ▂··●▂▄▂··▄▂▂ beats 3/12 easy · avg 75% 26% sharp · at its level · several runs
MBPP 37.4% █·▅▃●▃▅▃▃▅·▅ beats 6/15 easy · avg 81% 18% at its level · several runs
HumanEvalSynthesize (HumanEvalPac… 31.9% ▁·▂▁▂●▅█▄▄▁▂ beats 10/35 medium · avg 62% 12% at its level
CRUXEval-I 31.3% ▁··▁●▂█▇▁··▁ beats 4/22 medium · avg 56% 8.5% at its level · several runs
CRUXEval-O 34.2% ▁·▂●▁█▁▂···▁ beats 5/22 medium · avg 54% 7.5% blunt · at its level · several runs
DS-1000 (maker) 23.8% ▄▄·▄█●▄█·█·▄ beats 5/12 medium · avg 49% 7.2% typical
HumanEvalExplain (HumanEvalPack, … 15.3% ▄▇●▃▄▇▄▁▇▃·▁ beats 11/35 medium · avg 46% 6.3% typical
HumanEvalFix (HumanEvalPack, 6-la… 12.9% ▁·▁▁▄●█▄█▁▅▅ beats 11/35 medium · avg 51% 5.1% sharp
math · #372 of 431 · 7.0% of its score
Beats [GPT-3.5 Turbo (0125)](gpt-3-5-turbo-0125.md), [DBRX Instruct](dbrx-instruct.md), [Command R Plus (04-2024)](command-r-plus-04-2024.md) here, though they rank higher overall. Trails [OPT-1.3B](opt-1-3b.md), [OPT-2.7B](opt-2-7b.md), [Gemma 2B (base)](gemma-2b-base.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 21.5% ▄▄▄·●█·██·█▄ beats 3/12 easy · avg 75% 7.0% sharp · at its level
other tests (1)
test result vs the others in its run difficulty counts why
ODEX English pass@1 (zero-shot) 46.5% ▄▄········▄● beats 3/4 medium · avg 58% 1.4% blunt · at its level
source results share of the score
StarCoder 2 and The Stack v2: The Next … 4 47% ▇▇▇▇▇▇
Granite Code Models: A Family of Open F… 6 42% ▇▇▇▇▇
CRUXEval published evaluation results 2 5% ▇
StarCoder: may the source be with you! … 1 4% ▇
Execution-Based Evaluation for Open-Dom… 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 35.1
Licences: StarCoder 2 and The Stack v2: The Next Generation ([CC BY-SA 4.0](../../licenses/paper-2402.19173v1-CC-BY-SA-4.0.txt)) · Granite Code Models: A Family of Open Foundation Models for Code Intelligence ([CC BY 4.0](../../licenses/paper-2405.04324v1-CC-BY-4.0.txt)) · CRUXEval published evaluation results ([MIT](../../licenses/cruxeval-published-results-MIT.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 StarCoderBase 15.5B (Q4_K_M, needs ~10.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/starcoderbase-GGUF/tree/131a8462861633e1ce86a6da5d2881e1dd42b460))
```sh
llama cli -hf mradermacher/starcoderbase-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/starcoderbase-GGUF:Q4_K_M
```
Other versions: Q8_0 (~17 GB) `llama cli -hf mradermacher/starcoderbase-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 StarCoderBase 15.5B (Q4_K_M, needs ~10.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/starcoderbase-GGUF/tree/131a8462861633e1ce86a6da5d2881e1dd42b460))
```sh
lms get https://huggingface.co/mradermacher/starcoderbase-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/starcoderbase-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 StarCoderBase 15.5B (Q4_K_M, needs ~10.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/starcoderbase-GGUF/tree/131a8462861633e1ce86a6da5d2881e1dd42b460))
```sh
ollama run hf.co/mradermacher/starcoderbase-GGUF:Q4_K_M
```
Other versions: Q8_0 (~17 GB) `ollama run hf.co/mradermacher/starcoderbase-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (fp32, needs ~63.28 GB, from [huggingface.co/bigcode](https://huggingface.co/bigcode/starcoderbase/tree/88ec5781ad071a9d9e925cd28f327dea22eb5188))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='bigcode/starcoderbase', torch_dtype='auto', device_map='auto')('def print_hello_world():', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/bigcode](https://huggingface.co/bigcode/starcoderbase)
[interactive version](https://whatmodel.sh/model/starcoderbase) · [JSON](https://whatmodel.sh/api?format=json&all=1)