[whatmodel.sh](../../README.md) / WizardCoder Python 13B V1.0
# WizardCoder Python 13B V1.0
WizardLM · released 2023-08 · 13B params · llama2
**score 43.4** ±2.4 · #510 of 732 · from 3 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 WizardCoder Python 13B V1.0 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #355 of 469 97% 3
coding · #355 of 469 · 97% of its score
Beats [LFM2.5 2.6B](lfm2-5-2-6b.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.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), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval (maker) 64.0% ▁▁▁▁▂▂▆▆●▅▂▁ beats 120/205 easy · avg 79% 56% sharp · at its level · small run
CRUXEval-I 36.5% ▁··▁▅●█▇▁··▁ beats 7/22 medium · avg 52% 21% at its level
CRUXEval-O 41.3% ▁·▂▃▁●▁▂···▁ beats 13/22 medium · avg 51% 20% blunt · at its level
source results share of the score
WizardLM team vendor-reported scores fo… 1 56% ▇▇▇▇▇▇▇
CRUXEval published evaluation results 2 42% ▇▇▇▇▇
prior: pull toward the middle 3% ▇
weighted average = 43.4
Licences: CRUXEval published evaluation results ([MIT](../../licenses/cruxeval-published-results-MIT.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 Python 13B V1.0 (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/tree/6bf0d04a20f89fff726044df2c23f967df613b7f))
```sh
llama cli -hf TheBloke/WizardCoder-Python-13B-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 TheBloke/WizardCoder-Python-13B-V1.0-GGUF:Q4_K_M
```
Other versions: Q8_0 (~13.8 GB) `llama cli -hf TheBloke/WizardCoder-Python-13B-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 Python 13B V1.0 (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/tree/6bf0d04a20f89fff726044df2c23f967df613b7f))
```sh
lms get https://huggingface.co/TheBloke/WizardCoder-Python-13B-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 (~13.8 GB) `lms get https://huggingface.co/TheBloke/WizardCoder-Python-13B-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 Python 13B V1.0 (Q4_K_M, needs ~7.9 GB, from [ollama.com](https://ollama.com/library/wizardcoder/tags))
```sh
ollama run wizardcoder:13b-python-q4_K_M
```
Other versions: Q8_0 (~14 GB) `ollama run wizardcoder:13b-python-q8_0`
Model repository and documentation: [huggingface.co/WizardLM](https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0)
[interactive version](https://whatmodel.sh/model/wizardcoder-python-13b-v1-0) · [JSON](https://whatmodel.sh/api?format=json&all=1)