[whatmodel.sh](../../README.md) / Code Llama 34B (base)
# Code Llama 34B (base)
Meta · released 2023-08 · 33.74B params · llama2
**score 41.5** ±0.5 · #541 of 732 · from 23 benchmark results across 7 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Code Llama 34B (base) among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #379 of 469 86% 11
math ▁▁▃▅●▆▄█▄▂▁▁ #286 of 431* 4.8% 1
coding · #379 of 469 · 86% 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 [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [WizardLM 13B V1.2](wizardlm-13b-v1-2.md), [Granite 3.0 2B Instruct](granite-3-0-2b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 51.8% ▁·▁▁▃▄●▅█▃▁▁ beats 38/99 easy · avg 76% 37% sharp · at its level · several runs
HumanEval+ 43.9% ▁·▁▁▁▃▄●▄█▃▁ beats 35/99 easy · avg 70% 17% sharp · at its level
MBPP 48.6% █·▅▃▃▃▅▃▃●·▅ beats 13/15 easy · avg 71% 8.9% at its level · several runs
HumanEvalSynthesize (HumanEvalPac… 43.3% ▁·▂▁▂▆▅●▄▄▁▂ beats 23/35 medium · avg 62% 4.6% at its level
MBPP+ 56.3% ▁···▁▁▂▅●█▆▂ beats 32/75 easy · avg 66% 3.3% at its level
CRUXEval-I (maker) 47.2% ▁··▁▅▂█●▁··▁ beats 20/22 medium · avg 56% 3.0% at its level · several runs
HumanEvalExplain (HumanEvalPack, … 17.1% ▄▇●▃▄▇▄▁▇▃·▁ beats 13/35 medium · avg 46% 2.9% at its level
DS-1000 34.3% ▄▄·▄██▄█·●·▄ beats 11/12 medium · avg 49% 2.8% at its level
HumanEvalFix (HumanEvalPack, 6-la… 19.7% ▁·▁▁▄██●█▁▅▅ beats 19/35 medium · avg 51% 2.7% sharp · at its level
CRUXEval-O (maker) 42.4% ▁·▂▃▁●▁▂···▁ beats 15/22 medium · avg 54% 2.6% blunt · at its level · several runs
Tulu 2: CodexEval pass@10 77.6% ▃▃▃·▆▃▃█▃●▂▅ beats 21/25 easy · avg 83% 1.8% sharp · at its level
math · #286 of 431 · 4.8% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [Llama 3.3 70B Instruct](llama-3-3-70b.md), [DeepSeek-V2.5](deepseek-v2-5.md) here, though they rank higher overall. Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Gemma 7B (base)](gemma-7b-base.md), [Mistral 7B v0.1 (base)](mistral-7b-v0-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K 35.0% ▆▂▅●▅▅▃▆···▂ beats 12/25 easy · avg 75% 4.8% sharp · at its level · several runs
other tests (4)
test result vs the others in its run difficulty counts why
BIG-Bench Hard 57.0% ▁·····▂█●▅·▁ beats 16/25 easy · avg 74% 3.9% sharp · at its level
BigCodeBench (Complete) 37.1% ▂▁▁▄▂●▇█▆▂▂▂ beats 19/58 medium · avg 48% 2.9% at its level
MMLU 47.4% ▁▁▃▃●█▄▁█··▁ beats 8/25 medium · avg 73% 1.7% at its level
Tulu 2: TyDiQA gold passage, 1-sh… 57.1% ▂··▁▃▂▂▂█●▁▂ beats 22/25 medium · avg 59% 0.4% blunt · at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 42% ▇▇▇▇▇
StarCoder 2 and The Stack v2: The Next … 4 17% ▇▇
Granite Code Models: A Family of Open F… 6 16% ▇▇
Code Llama: Open Foundation Models for … 2 10% ▇
Tulu 2 5 10% ▇
BigCodeBench paper 1 3% ▇
CRUXEval published evaluation results (… 2 2% ▇
prior: pull toward the middle 0% ▇
weighted average = 41.5
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · 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)) · Code Llama: Open Foundation Models for Code ([CC BY 4.0](../../licenses/paper-2308.12950v3-CC-BY-4.0.txt)) · Tulu 2 ([CC BY 4.0](../../licenses/CC-BY-4.0.txt)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · 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 Code Llama 34B (base) (Q4_K_M, needs ~20.2 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/CodeLlama-34B-GGUF/tree/6cfc6e717f9a3725d7a26246bc1d4b7eaca6dd4b))
```sh
llama cli -hf TheBloke/CodeLlama-34B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/CodeLlama-34B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~35.9 GB) `llama cli -hf TheBloke/CodeLlama-34B-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 Code Llama 34B (base) (Q4_K_M, needs ~20.2 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/CodeLlama-34B-GGUF/tree/6cfc6e717f9a3725d7a26246bc1d4b7eaca6dd4b))
```sh
lms get https://huggingface.co/TheBloke/CodeLlama-34B-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/CodeLlama-34B-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 Code Llama 34B (base) (Q4_K_M, needs ~20 GB, from [ollama.com](https://ollama.com/library/codellama/tags))
```sh
ollama run codellama:34b-code-q4_K_M
```
Other versions: Q8_0 (~36 GB) `ollama run codellama:34b-code-q8_0`
Model repository and documentation: [huggingface.co/codellama](https://huggingface.co/codellama/CodeLlama-34b-hf)
[interactive version](https://whatmodel.sh/model/codellama-34b) · [JSON](https://whatmodel.sh/api?format=json&all=1)