[whatmodel.sh](../../README.md) / WizardCoder 33B V1.1
# WizardCoder 33B V1.1
WizardLM · released 2024-01 · 33.34B params · msft-research
**score 50.9** ±0.7 · #351 of 732 · from 5 benchmark results across 3 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks WizardCoder 33B V1.1 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #269 of 469 83% 2
coding · #269 of 469 · 83% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [QwQ-32B-Preview](qwq-32b-preview.md), [Phi-3 Medium 128K Instruct](phi-3-medium-128k-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 79.9% ▁·▁▁▃▄▆▅●▃▁▁ beats 83/99 easy · avg 76% 55% sharp · several runs
HumanEval+ 73.2% ▁·▁▁▁▃▄▄▄●▃▁ beats 82/99 easy · avg 70% 28% sharp · at its level
other tests (1)
test result vs the others in its run difficulty counts why
EvoEval (overall) 53.3% ▂▂▃▄▄█▁▄▅●▁▃ beats 38/42 medium · avg 52% 17% at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 2 59% ▇▇▇▇▇▇▇
Top Leaderboard Ranking = Top Coding Pr… 2 36% ▇▇▇▇
WizardLM team vendor-reported scores fo… 1 4% ▇
prior: pull toward the middle 0% ▇
weighted average = 50.9
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))
## 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 33B V1.1 (Q4_K_M, needs ~19.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/WizardCoder-33B-V1.1-GGUF/tree/b51c35c9b4e30a57672c7232de8e90e0841b00e2))
```sh
llama cli -hf TheBloke/WizardCoder-33B-V1.1-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-33B-V1.1-GGUF:Q4_K_M
```
Other versions: Q8_0 (~35.4 GB) `llama cli -hf TheBloke/WizardCoder-33B-V1.1-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 WizardCoder 33B V1.1 (Q4_K_M, needs ~19.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/WizardCoder-33B-V1.1-GGUF/tree/b51c35c9b4e30a57672c7232de8e90e0841b00e2))
```sh
lms get https://huggingface.co/TheBloke/WizardCoder-33B-V1.1-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 (~35.4 GB) `lms get https://huggingface.co/TheBloke/WizardCoder-33B-V1.1-GGUF@Q8_0 -y` · 8bit (~35.4 GB, Mac only) `lms get https://huggingface.co/GGorman/WizardCoder-33B-V1.1-Q8-mlx -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 33B V1.1 (Q4_K_M, needs ~20 GB, from [ollama.com](https://ollama.com/library/wizardcoder/tags))
```sh
ollama run wizardcoder:33b-v1.1-q4_K_M
```
Other versions: Q8_0 (~35 GB) `ollama run wizardcoder:33b-v1.1-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run WizardCoder 33B V1.1 (8bit, needs ~35.4 GB, Mac only, from [huggingface.co/GGorman](https://huggingface.co/GGorman/WizardCoder-33B-V1.1-Q8-mlx/tree/07799d1270228467360df2d7a122796eb1b96717))
```sh
mlx_lm.chat --model GGorman/WizardCoder-33B-V1.1-Q8-mlx
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model GGorman/WizardCoder-33B-V1.1-Q8-mlx
```
Model repository and documentation: [huggingface.co/WizardLM](https://huggingface.co/WizardLM/WizardCoder-33B-V1.1)
[interactive version](https://whatmodel.sh/model/wizardcoder-33b-v1-1) · [JSON](https://whatmodel.sh/api?format=json&all=1)