[whatmodel.sh](../../README.md) / OpenLLaMA 7B v2
# OpenLLaMA 7B v2
OpenLM Research · released 2023-07 · apache-2.0
**score 29.6** ±1.0 · #666 of 732 · from 12 benchmark results across 2 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks OpenLLaMA 7B v2 among every model on that task or test, worst to best.
other tests (12)
test result vs the others in its run difficulty counts why
ARC Challenge 42.4% ▁·▁··▁▁▇▂●▆▃ beats 17/26 easy · avg 82% 48% sharp
WinoGrande 65.8% ▁····▁··▁▂●▄ beats 13/26 easy · avg 86% 11% at its level
BoolQ 71.4% ▂▃▂▅▆▂▅●█▂▂▂ beats 15/26 easy · avg 88% 8.5% at its level
HellaSwag 74.6% ▃···▁▇▅█●▃▁▁ beats 18/26 very easy · avg 91% 6.1% at its level
ARC Easy 69.7% ▁▁·▁▁▄▅▄●▅▁▃ beats 18/26 easy · avg 85% 5.8% blunt · at its level
SciQ 93.8% ▁·▁·▁·▃▅▃●▄▁ beats 19/26 very easy · avg 97% 4.7% blunt
PIQA 79.2% ▁·▁··▁▁▃█▅●▂ beats 23/26 easy · avg 87% 4.2% blunt · at its level
OpenBookQA 30.2% █·▁▁▃·▁▃●▇▃▄ beats 14/26 hard · avg 49% 3.8% too hard for it
LAMBADA (OpenAI version), last-wo… 71.0% ▁··▂▁▁▁▂▆●▅▃ beats 18/26 very easy · avg 86% 2.6% blunt · at its level
Social IQA 42.0% ▁···▁▃●▁▁▁·▁ beats 10/26 hard · avg 56% 2.4% blunt
Open PT-LLM Leaderboard average (… 39.2% ▃▁▁▁▁●▁▁▂█▅▁ beats 47/276 medium · avg 72% 1.7% noisy
TruthfulQA MC2 34.6% █·●·█▄···▄·█ beats 2/9 medium · avg 55% 0.4% blunt · at its level
source results share of the score
Stability AI 11 98% ▇▇▇▇▇▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard 1 2% ▇
prior: pull toward the middle 0% ▇
weighted average = 29.6
Licences: Stability AI ([Apache-2.0](../../licenses/stablelm-original-eval-json-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.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 OpenLLaMA 7B v2 (Q8_0, needs ~7.2 GB, from [huggingface.co/exocet25](https://huggingface.co/exocet25/open_llama_7b_v2-Q8_0-GGUF/tree/b5def4aaf21a24f437346bdb9ba42d738f226bd7))
```sh
llama cli -hf exocet25/open_llama_7b_v2-Q8_0-GGUF:q8_0
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf exocet25/open_llama_7b_v2-Q8_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 OpenLLaMA 7B v2 (Q8_0, needs ~7.2 GB, from [huggingface.co/exocet25](https://huggingface.co/exocet25/open_llama_7b_v2-Q8_0-GGUF/tree/b5def4aaf21a24f437346bdb9ba42d738f226bd7))
```sh
lms get https://huggingface.co/exocet25/open_llama_7b_v2-Q8_0-GGUF@q8_0 -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run OpenLLaMA 7B v2 (Q8_0, needs ~7.2 GB, from [huggingface.co/exocet25](https://huggingface.co/exocet25/open_llama_7b_v2-Q8_0-GGUF/tree/b5def4aaf21a24f437346bdb9ba42d738f226bd7))
```sh
ollama run hf.co/exocet25/open_llama_7b_v2-Q8_0-GGUF:q8_0
```
### Other ways to run it
1. Run it as published (fp16, needs ~13.48 GB, from [huggingface.co/openlm-research](https://huggingface.co/openlm-research/open_llama_7b_v2/tree/e5961def23172a2384543940e773ab676033c963))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='openlm-research/open_llama_7b_v2', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/openlm-research](https://huggingface.co/openlm-research/open_llama_7b_v2)
[interactive version](https://whatmodel.sh/model/open-llama-7b-v2) · [JSON](https://whatmodel.sh/api?format=json&all=1)