[whatmodel.sh](../../README.md) / WizardCoder Python 34B V1.0 # WizardCoder Python 34B V1.0 WizardLM · released 2023-08 · 34B params · llama2 **score 47.2** ±0.7 · #429 of 732 · from 8 benchmark results across 4 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 34B V1.0 among every model on that task or test, worst to best. 
task    vs the others         rank  counts  tests
coding  ▁▁▁▃▅●█▇▅▄▃▁   #316 of 469     87%      5
coding · #316 of 469 · 87% of its score Beats [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3-VL 30B-A3B Instruct](qwen3-vl-30b-a3b-instruct.md), [Qwen3 4B Instruct 2507](qwen3-4b-instruct-2507.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), [Yi 1.5 9B Chat](yi-1-5-9b-chat.md) here, though they rank lower overall. 
test        result  vs the others in its run  difficulty        counts  why
HumanEval    73.2%  ▁·▁▁▃▄▆▅●▃▁▁ beats 66/99  easy · avg 76%       53%  sharp · at its level · several runs
HumanEval+   64.6%  ▁·▁▁▁▃▄▄▄●▃▁ beats 59/99  easy · avg 70%       26%  sharp · at its level
MBPP+        63.2%  ▁···▁▁▂▅▇●▆▂ beats 44/75  easy · avg 66%      5.1%  at its level
CRUXEval-I   42.7%  ▁··▁▅▂█●▁··▁ beats 15/22  medium · avg 52%    1.7%  at its level
CRUXEval-O   43.4%  ▁·▂▃▁●▁▂···▁ beats 16/22  medium · avg 51%    1.6%  blunt · at its level
other tests (1) 
test               result  vs the others in its run  difficulty        counts  why
EvoEval (overall)   40.8%  ▂▂▃▄▄█▁●▅▃▁▃ beats 27/42  medium · avg 52%     13%  at its level
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
EvalPlus / EvalPerf published evaluatio…        3   61% ▇▇▇▇▇▇▇
Top Leaderboard Ranking = Top Coding Pr…        2   31% ▇▇▇▇
WizardLM team vendor-reported scores fo…        1    4% ▇
CRUXEval published evaluation results           2    3% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 47.2
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)) · 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 34B V1.0 (Q4_K_M, needs ~20.2 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/WizardCoder-Python-34B-V1.0-GGUF/tree/19e229180b810e370d279a6d751becc664c65138)) ```sh llama cli -hf TheBloke/WizardCoder-Python-34B-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-34B-V1.0-GGUF:Q4_K_M ``` Other versions: Q8_0 (~35.9 GB) `llama cli -hf TheBloke/WizardCoder-Python-34B-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 34B V1.0 (Q4_K_M, needs ~20.2 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/WizardCoder-Python-34B-V1.0-GGUF/tree/19e229180b810e370d279a6d751becc664c65138)) ```sh lms get https://huggingface.co/TheBloke/WizardCoder-Python-34B-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 (~35.9 GB) `lms get https://huggingface.co/TheBloke/WizardCoder-Python-34B-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 34B V1.0 (Q4_K_M, needs ~20 GB, from [ollama.com](https://ollama.com/library/wizardcoder/tags)) ```sh ollama run wizardcoder:34b-python-q4_K_M ``` Other versions: Q8_0 (~36 GB) `ollama run wizardcoder:34b-python-q8_0` Model repository and documentation: [huggingface.co/WizardLM](https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0) [interactive version](https://whatmodel.sh/model/wizardcoder-python-34b-v1-0) · [JSON](https://whatmodel.sh/api?format=json&all=1)