[whatmodel.sh](../../README.md) / WizardCoder-15B-V1.0
# WizardCoder-15B-V1.0
WizardLM · released 2023-06 · bigscience-openrail-m
**score 43.1** ±0.8 · #517 of 732 · from 3 benchmark results across 1 source
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks WizardCoder-15B-V1.0 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #364 of 469 100% 3
coding · #364 of 469 · 100% of its score
Beats [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md) here, though they rank higher overall. Trails [Code Tulu 2 13B](code-tulu-2-13b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [Code Llama 34B Python](codellama-34b-python.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 56.7% ▁·▁▁▃▄●▅█▃▁▁ beats 44/99 easy · avg 76% 51% sharp · at its level
HumanEval+ 50.6% ▁·▁▁▁▃▄●▄█▃▁ beats 43/99 easy · avg 70% 41% sharp · at its level
MBPP+ 54.2% ▁···▁▁▂▅●█▆▂ beats 24/75 easy · avg 66% 7.9% at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 100% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 0% ▇
weighted average = 43.1
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.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 WizardCoder-15B-V1.0 (Q4_K_M, needs ~10.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/WizardCoder-15B-V1.0-GGUF/tree/e3562bc7e44f17e8cf81180b5c0820847737c292))
```sh
llama cli -hf mradermacher/WizardCoder-15B-V1.0-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/WizardCoder-15B-V1.0-GGUF:Q4_K_M
```
Other versions: Q8_0 (~17 GB) `llama cli -hf mradermacher/WizardCoder-15B-V1.0-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 WizardCoder-15B-V1.0 (Q4_K_M, needs ~10.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/WizardCoder-15B-V1.0-GGUF/tree/e3562bc7e44f17e8cf81180b5c0820847737c292))
```sh
lms get https://huggingface.co/mradermacher/WizardCoder-15B-V1.0-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/WizardCoder-15B-V1.0-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 WizardCoder-15B-V1.0 (Q4_K_M, needs ~10.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/WizardCoder-15B-V1.0-GGUF/tree/e3562bc7e44f17e8cf81180b5c0820847737c292))
```sh
ollama run hf.co/mradermacher/WizardCoder-15B-V1.0-GGUF:Q4_K_M
```
Other versions: Q8_0 (~17 GB) `ollama run hf.co/mradermacher/WizardCoder-15B-V1.0-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (fp16, needs ~31.04 GB, from [huggingface.co/WizardLMTeam](https://huggingface.co/WizardLMTeam/WizardCoder-15B-V1.0/tree/9c177589dec389eac2c8de51cbc371d45e47984e))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='WizardLMTeam/WizardCoder-15B-V1.0', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/WizardLMTeam](https://huggingface.co/WizardLMTeam/WizardCoder-15B-V1.0)
[interactive version](https://whatmodel.sh/model/wizardcoder-15b-v1-0) · [JSON](https://whatmodel.sh/api?format=json&all=1)