[whatmodel.sh](../../README.md) / Llama 2 13B (base)
# Llama 2 13B (base)
Meta · released 2023-07 · 13B params · llama2
**score 35.6** ±0.6 · #621 of 732 · from 55 benchmark results across 10 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Llama 2 13B (base) among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁●▃▅▇█▇▅▄▃▁ #452 of 469 15% 3
math ▁▁▃▅●▆▄█▄▂▁▁ #335 of 431 12% 2
coding · #452 of 469 · 15% of its score
Beats [Molmo 7B-D (0924)](molmo-7b-d.md), [Tulu 2 7B](tulu-2-7b.md), [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md) here, though they rank higher overall. Trails [Falcon3 1B Instruct](falcon3-1b.md), [Granite 3.0 1B-A400M Instruct](granite-3-0-1b-a400m.md), [StarCoderBase 3B](starcoderbase-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 18.9% █···●·███··█ beats 1/5 medium · avg 61% 8.8% sharp · several runs
MBPP 35.4% ▄··●·▄··█··▄ beats 1/5 easy · avg 69% 4.4% at its level · several runs
Tulu 2: CodexEval pass@10 32.5% ▃●▃·▆▃▃█▃▂▂▅ beats 2/25 easy · avg 83% 2.2% sharp · at its level
math · #335 of 431 · 12% of its score
Beats [GPT-4 (0613)](gpt-4-0613.md), [Athene-70B](athene-70b.md), [Llama 3 70B Instruct](llama-3-70b.md) here, though they rank higher overall. Trails [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md), [Zephyr 7B beta](zephyr-7b-beta.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K 25.0% ▆▂●█▅▅▃▆···▂ beats 7/25 easy · avg 82% 9.2% sharp · at its level · several runs
MATH 6.0% █·●·█·██···█ beats 1/5 hard · avg 27% 2.6% several runs
other tests (23)
test result vs the others in its run difficulty counts why
ARC Challenge 49.2% ▁·▁··▁▁▇▂█▆● beats 25/26 easy · avg 82% 25% sharp · at its level · several runs
MMLU 52.0% ▁▁▃▃▄●▄▁█··▁ beats 12/25 easy · avg 82% 8.2% at its level · several runs
BIG-Bench Hard 48.9% ▁·····▂●▂▅·▁ beats 9/25 medium · avg 65% 7.1% sharp · several runs
WinoGrande 72.2% ▁····▁··▁▂█● beats 24/26 easy · avg 86% 6.3% at its level · several runs
Natural Questions (closed book) 32.2% ▁·▁·▁▂▁▂·▅▂● beats 28/28 medium · avg 48% 5.0% several runs
HellaSwag 79.4% ▃···▁▇▅█▇●▁▁ beats 23/26 very easy · avg 91% 3.6% at its level · several runs
TriviaQA 9.5% ▁··▁●▃▃▁▄▅▄█ beats 5/28 easy · avg 83% 2.7% at its level · several runs
ARC Easy 77.6% ▁▁·▁▁▄▅▄█▅▁● beats 25/26 easy · avg 85% 2.6% blunt · at its level · several runs
BoolQ 80.5% ▂▃▂▅▆▂▅▆█▂●▂ beats 25/26 easy · avg 88% 2.4% at its level
PIQA 79.1% ▁·▁··▁▁▃█▅●▂ beats 22/26 easy · avg 87% 1.9% blunt · at its level · several runs
OpenBookQA 35.4% █·▁▁▃·▁▃▅▇▃● beats 24/26 hard · avg 49% 1.7% typical
VEC embodied concepts, zero-shot … 54.6% ▁····▁▁·▁·●█ beats 4/11 hard · avg 63% 1.4% typical
SciQ 94.5% ▁·▁·▁·▃▅▃█●▁ beats 22/26 very easy · avg 97% 1.0% blunt · maxed out
Social IQA 42.8% ▁···▁▃█●▁▁·▁ beats 20/26 hard · avg 56% 0.8% blunt
LAMBADA (OpenAI version), last-wo… 76.8% ▁··▂▁▁▁▂▆█▅● beats 26/26 very easy · avg 86% 0.6% blunt
Open PT-LLM Leaderboard average (… 57.5% ▃▁▁▁▁▁▁▁●█▅▁ beats 83/276 medium · avg 72% 0.6% at its level · noisy
C-Eval 37.3% ▁···▁●▂▁▂▂▃█ beats 2/33 medium · avg 59% 0.6% noisy
EuroEval Life in the UK (English … 74.0% ▁▁▁▁▁▁▁▂●█▄▃ beats 47/146 easy · avg 82% 0.5% at its level · noisy
Tulu 2: TyDiQA gold passage, 1-sh… 56.5% ▂··▁▃▂▂▂█●▁▂ beats 21/25 medium · avg 59% 0.5% blunt · at its level
HaluEval QA hallucination detecti… 70.0% █▁▁·▁▁·▁▁▁▁● beats 27/27 hard · avg 66% 0.2% noisy
COPA 85.0% ▅·▃·▃··●▃··▃ beats 6/8 easy · avg 92% 0.1% at its level · noisy
TruthfulQA MC2 36.9% ▅●█▆▁▂▅···▁▁ beats 5/31 medium · avg 57% 0.1% blunt · at its level
VEC visual concepts, zero-shot pe… 62.8% ▅▅●█·▃··▃·▃▃ beats 4/11 hard · avg 66% 0.0% blunt · at its level
source results share of the score
Stability AI 10 32% ▇▇▇▇
Mixtral of Experts 12 24% ▇▇▇
MiniCPM: Unveiling the Potential of Sma… 7 15% ▇▇
Mistral 7B 12 13% ▇▇
Tulu 2 5 10% ▇
Hallucinations Leaderboard results 4 4% ▇
Can Language Models Understand Physical… 2 1% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
EuroEval leaderboards 1 1% ▇
From Test-Taking to Test-Making: Examin… 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 35.6
Licences: Stability AI ([Apache-2.0](../../licenses/stablelm-original-eval-json-Apache-2.0.txt)) · Mixtral of Experts ([CC BY 4.0](../../licenses/CC-BY-4.0.txt)) · MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Mistral 7B ([CC BY 4.0](../../licenses/paper-2310.06825v1-CC-BY-4.0.txt)) · Tulu 2 ([CC BY 4.0](../../licenses/CC-BY-4.0.txt)) · Hallucinations Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Can Language Models Understand Physical Concepts? ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-726-CC-BY-4-0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · From Test-Taking to Test-Making: Examining LLM Authoring of Commonsense Assessment Items ([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 Llama 2 13B (base) (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/tree/b106d1c018ac999af9130b83134fb6b7c5331dea))
```sh
llama cli -hf TheBloke/Llama-2-13B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/Llama-2-13B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~13.8 GB) `llama cli -hf TheBloke/Llama-2-13B-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 Llama 2 13B (base) (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Llama-2-13B-GGUF/tree/b106d1c018ac999af9130b83134fb6b7c5331dea))
```sh
lms get https://huggingface.co/TheBloke/Llama-2-13B-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/Llama-2-13B-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 Llama 2 13B (base) (Q4_0, needs ~7.4 GB, from [ollama.com](https://ollama.com/library/llama2/tags))
```sh
ollama run llama2:13b-text
```
Model repository and documentation: [huggingface.co/meta-llama](https://huggingface.co/meta-llama/Llama-2-13b-hf)
[interactive version](https://whatmodel.sh/model/llama-2-13b) · [JSON](https://whatmodel.sh/api?format=json&all=1)