[whatmodel.sh](../../README.md) / Llama 2 7B (base)
# Llama 2 7B (base)
Meta · released 2023-07 · 6.74B params · llama2
**score 31.8** ±0.5 · #652 of 732 · from 73 benchmark results across 12 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 7B (base) among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁●▃▅▇█▇▅▄▃▁ #459 of 469 12% 3
math ▁▁▃●█▆▄█▄▂▁▁ #379 of 431 8.5% 2
coding · #459 of 469 · 12% of its score
Beats [Molmo 7B-D (0924)](molmo-7b-d.md), [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank higher overall. Trails [Phi-1 (1.3B)](phi-1.md), [GPT-J 6B](gpt-j-6b.md), [OpenAssistant SFT-4 Pythia 12B](oasst-sft-4-pythia-12b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 11.6% ●···█·███··█ beats 0/5 medium · avg 61% 6.1% sharp · several runs
MBPP 26.1% ●··▄·▄··█··▄ beats 0/5 easy · avg 69% 3.6% at its level · several runs
Tulu 2: CodexEval pass@10 26.8% ●▃▃·▆▃▃█▃▂▂▅ beats 1/25 easy · avg 83% 1.8% sharp · at its level
math · #379 of 431 · 8.5% of its score
Beats [Command R Plus (04-2024)](command-r-plus-04-2024.md), [Yi 34B Chat](yi-34b-chat.md), [Llama 3 8B Instruct](llama-3-8b.md) here, though they rank higher overall. Trails [GPT-Neo 1.3B](gpt-neo-1-3b.md), [OPT-1.3B](opt-1-3b.md), [OPT-2.7B](opt-2-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K 12.0% ●▂▅█▅▅▃▆···▂ beats 2/25 easy · avg 82% 6.8% sharp · at its level · several runs
MATH 3.9% ●·█·█·██···█ beats 0/5 hard · avg 27% 1.7% too hard for it · several runs
other tests (24)
test result vs the others in its run difficulty counts why
ARC Challenge 46.2% ▁·▁··▁▁▇▂█●▃ beats 21/26 easy · avg 82% 25% sharp · at its level · several runs
WinoGrande 69.6% ▁····▁··▁▂█● beats 22/26 easy · avg 86% 8.9% at its level · several runs
MMLU 41.8% ▁▁●▃▄█▄▁█··▁ beats 3/25 easy · avg 82% 6.4% at its level · several runs
HellaSwag 75.9% ▃···▁▇▅█●▃▁▁ beats 19/26 very easy · avg 91% 6.0% at its level · several runs
BIG-Bench Hard 39.3% ▁·····●█▂▅·▁ beats 2/25 medium · avg 65% 4.7% sharp · several runs
BoolQ 77.7% ▂▃▂▅▆▂▅▆█●▂▂ beats 24/26 easy · avg 88% 4.3% at its level · several runs
ARC Easy 74.5% ▁▁·▁▁▄▅▄█▅●▃ beats 24/26 easy · avg 85% 4.1% blunt · at its level · several runs
Natural Questions (closed book) 27.1% ▁·▁·▁▂▁▂·▅▂● beats 23/28 medium · avg 48% 3.8% several runs
OpenBookQA 31.4% █·▁▁▃·▁▃▅●▃▄ beats 19/26 easy · avg 81% 3.7% at its level · several runs
PIQA 77.7% ▁·▁··▁▁▃█●█▂ beats 17/26 easy · avg 87% 2.8% blunt · at its level · several runs
TriviaQA 9.2% ▁··▁●▃▃▁▄▅▄█ beats 4/28 easy · avg 83% 2.4% at its level · several runs
SciQ 93.6% ▁·▁·▁·▃▅▃●▄▁ beats 17/26 very easy · avg 97% 1.9% blunt · several runs
Social IQA 43.5% ▁···▁▃█●▁▁·▁ beats 21/26 medium · avg 60% 1.3% blunt · several runs
VEC embodied concepts, zero-shot … 55.4% ▁····▁▁·▁·▃● beats 7/11 hard · avg 63% 1.0% typical
LAMBADA (OpenAI version), last-wo… 73.5% ▁··▂▁▁▁▂▆█●▃ beats 21/26 very easy · avg 86% 0.6% blunt · at its level
Open PT-LLM Leaderboard average (… 48.9% ▃▁▁▁▁▁▁●▂█▅▁ beats 65/276 medium · avg 72% 0.5% noisy
EuroEval Life in the UK (English … 52.8% ▁▁▁▁▁▁●▂▄█▄▃ beats 21/146 easy · avg 82% 0.4% at its level · noisy
C-Eval 32.4% ▁···●▁▂▁▂▂▃█ beats 1/33 medium · avg 59% 0.4% noisy
Tulu 2: TyDiQA gold passage, 1-sh… 51.2% ▂··▁▃▂▂▂●▅▁▂ beats 12/25 medium · avg 59% 0.4% blunt · at its level
EuroEval HellaSwag (English), tes… 31.3% ▃·▁▁●▁▂▅█▇▄▁ beats 13/100 medium · avg 67% 0.3% sharp · noisy
TruthfulQA MC2 39.0% ▅●█▆▁▂▅···▁▁ beats 7/31 medium · avg 55% 0.2% blunt · at its level · several runs
COPA 82.6% ▅·▃·▃··●▃··▃ beats 4/8 easy · avg 92% 0.1% at its level · noisy
HaluEval QA hallucination detecti… 45.7% ●▁▁·▁▁·▁▁▁▁▁ beats 5/27 hard · avg 66% 0.1% too hard for it · noisy
VEC visual concepts, zero-shot pe… 61.4% ●▅▃█·▃··▃·▃▃ beats 0/11 hard · avg 66% 0.0% blunt · at its level
source results share of the score
Stability AI 11 26% ▇▇▇
Mixtral of Experts 12 19% ▇▇
OLMo: Accelerating the Science of Langu… 8 12% ▇
MiniCPM: Unveiling the Potential of Sma… 7 11% ▇
Mistral 7B 12 10% ▇
Textbooks Are All You Need II: phi-1.5 … 8 10% ▇
Tulu 2 5 7% ▇
Hallucinations Leaderboard results 4 3% ▇
Can Language Models Understand Physical… 2 1% ▇
EuroEval leaderboards 2 1% ▇
Open Portuguese LLM Leaderboard 1 0% ▇
From Test-Taking to Test-Making: Examin… 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 31.8
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)) · OLMo: Accelerating the Science of Language Models ([CC BY 4.0](../../licenses/paper-2402.00838v4-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)) · Textbooks Are All You Need II: phi-1.5 technical report ([CC BY 4.0](../../licenses/paper-2309.05463v1-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)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · 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 7B (base) (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/tree/b4e04e128f421c93a5f1e34ac4d7ca9b0af47b80))
```sh
llama cli -hf TheBloke/Llama-2-7B-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-2-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.2 GB) `llama cli -hf TheBloke/Llama-2-7B-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 7B (base) (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/tree/b4e04e128f421c93a5f1e34ac4d7ca9b0af47b80))
```sh
lms get https://huggingface.co/TheBloke/Llama-2-7B-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 (~7.2 GB) `lms get https://huggingface.co/TheBloke/Llama-2-7B-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 7B (base) (Q4_0, needs ~3.8 GB, from [ollama.com](https://ollama.com/library/llama2/tags))
```sh
ollama run llama2:7b-text
```
Model repository and documentation: [huggingface.co/meta-llama](https://huggingface.co/meta-llama/Llama-2-7b-hf)
[interactive version](https://whatmodel.sh/model/llama-2-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)