[whatmodel.sh](../../README.md) / Code Llama 34B Instruct
# Code Llama 34B Instruct
Meta · released 2023-08 · 33.7B params · llama2
**score 41.6** ±0.7 · #538 of 732 · from 16 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 Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #369 of 469 57% 6
chat & writing ▁▁▁●▆▄▆█▆▇▇▃ #367 of 402* 8.8% 2
math ▁▁▃▅●▆▄█▄▂▁▁ #301 of 431* 3.4% 1
coding · #369 of 469 · 57% 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), [Code Tulu 2 13B](code-tulu-2-13b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 51.8% ▁█▄▅▇●·▄▇▅▄▄ beats 23/42 easy · avg 75% 32% sharp · at its level · several runs
HumanEvalSynthesize (HumanEvalPac… 41.3% ▁·▂▁▂▆▅●▄▄▁▂ beats 19/35 medium · avg 62% 7.6% at its level
MBPP (maker) 57.0% ▃▃▃█··▃●··▅▃ beats 8/11 easy · avg 71% 5.4% at its level
HumanEvalExplain (HumanEvalPack, … 30.3% ▄▇█▃▄▇●▁▇▃·▁ beats 26/35 medium · avg 46% 4.7% at its level
HumanEvalFix (HumanEvalPack, 6-la… 28.2% ▁·▁▁▄██▄●▁▅▅ beats 23/35 medium · avg 51% 4.4% sharp · at its level
Tulu 2: CodexEval pass@10 76.5% ▃▃▃·▆▃▃█●▂▂▅ beats 20/25 easy · avg 83% 2.9% sharp · at its level
chat & writing · #367 of 402 · 8.8% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 350M](granite-4-0-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1136.9 ▁▁▁●▃▃▃▆▆▇█▃ beats 29/385 avg 1260 4.6% blunt
Polyrating rating (bias-aware re-… 1070.0 ▂▁▂▁▃●█▆▇▄▂▂ beats 33/95 avg 1187 4.2% typical
math · #301 of 431 · 3.4% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [DeepSeek-Coder-V2 Instruct (0724)](deepseek-coder-v2-0724.md), [Llama 3.1 Nemotron 70B Instruct](llama-3-1-nemotron-70b.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 38.0% ▆▂▅█●▅▃▆···▂ beats 13/25 easy · avg 72% 3.4% sharp · at its level
other tests (6)
test result vs the others in its run difficulty counts why
EvoEval (overall) 33.3% ▂▂▃▄▄●▁▄▅▃▁▃ beats 21/42 medium · avg 52% 12% at its level
BIG-Bench Hard 59.2% ▁·····▂█●▅·▁ beats 17/25 easy · avg 74% 6.4% sharp · at its level
BigCodeBench (Complete) 35.1% ▂▁▁▄▂●▇█▆▂▂▂ beats 15/58 medium · avg 48% 4.7% at its level
BigCodeBench (Instruct) 28.9% ▃▂·●▆██▆▅▃▃▃ beats 5/33 medium · avg 39% 4.5% at its level
MMLU 50.9% ▁▁▃▃▄●▄▁█··▁ beats 11/25 medium · avg 73% 2.8% at its level
Tulu 2: TyDiQA gold passage, 1-sh… 55.1% ▂··▁▃▂▂▂█●▁▂ beats 19/25 medium · avg 59% 0.7% blunt · at its level
source results share of the score
Top Leaderboard Ranking = Top Coding Pr… 2 32% ▇▇▇▇
Code Llama: Open Foundation Models for … 2 17% ▇▇
Granite Code Models: A Family of Open F… 3 17% ▇▇
Tulu 2 5 16% ▇▇
BigCodeBench paper 2 9% ▇
LMArena leaderboard dataset 1 5% ▇
Polyrating: A Cost-Effective and Bias-A… 1 4% ▇
prior: pull toward the middle 0% ▇
weighted average = 41.6
Licences: Top Leaderboard Ranking = Top Coding Proficiency ([CC BY 4.0](../../licenses/paper-2403.19114v1-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)) · 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)) · 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/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/))
## 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 Instruct (Q4_K_M, needs ~20.2 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/CodeLlama-34b-Instruct-hf-GGUF/tree/3238409c5878bdb4eaf71f1892f0da6a4ae8843f))
```sh
llama cli -hf mradermacher/CodeLlama-34b-Instruct-hf-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/CodeLlama-34b-Instruct-hf-GGUF:Q4_K_M
```
Other versions: Q8_0 (~35.9 GB) `llama cli -hf mradermacher/CodeLlama-34b-Instruct-hf-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 Instruct (Q4_K_M, needs ~20.2 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/CodeLlama-34b-Instruct-hf-GGUF/tree/3238409c5878bdb4eaf71f1892f0da6a4ae8843f))
```sh
lms get https://huggingface.co/mradermacher/CodeLlama-34b-Instruct-hf-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/mradermacher/CodeLlama-34b-Instruct-hf-GGUF@Q8_0 -y` · 4bit (~19.4 GB, Mac only) `lms get https://huggingface.co/mlx-community/CodeLlama-34b-Instruct-hf-4bit -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 Instruct (Q4_K_M, needs ~20.72 GB, from [ollama.com](https://ollama.com/library/codellama/tags))
```sh
ollama run codellama:34b-instruct-q4_K_M
```
Other versions: Q8_0 (~40.94 GB) `ollama run codellama:34b-instruct-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 Code Llama 34B Instruct (4bit, needs ~19.4 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/CodeLlama-34b-Instruct-hf-4bit/tree/7f372a865fbe23d97ada15f195092711bc7f56b4))
```sh
mlx_lm.chat --model mlx-community/CodeLlama-34b-Instruct-hf-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/CodeLlama-34b-Instruct-hf-4bit
```
Model repository and documentation: [huggingface.co/codellama](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf)
[interactive version](https://whatmodel.sh/model/codellama-34b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)