[whatmodel.sh](../../README.md) / Orca 2 13B
# Orca 2 13B
Microsoft · released 2023-11 · other (microsoft-research-license)
**score 34.5** ±2.8 · #633 of 732 · from 5 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 Orca 2 13B among every model on that task or test, worst to best.
other tests (5)
test result vs the others in its run difficulty counts why
Natural Questions (closed book) 17.8% ▁·▁·▁▂▁▂·●▂█ beats 15/28 medium · avg 48% 69% typical
Vectara hallucination leaderboard… 97.5% ▁▁▂▃▄▄█▆●▂▂▁ beats 114/140 very easy · avg 94% 10% blunt · noisy
TriviaQA 9.8% ▁··▁●▃▃▁▄▅▄█ beats 6/28 easy · avg 73% 10% at its level · noisy
HaluEval QA hallucination detecti… 66.2% █▁▁·▁▁·▁▁▁▁● beats 26/27 hard · avg 66% 5.0% noisy
TruthfulQA MC2 55.8% ▅▃█▆▁▂●···▁▁ beats 27/31 medium · avg 57% 2.3% blunt · at its level
source results share of the score
Hallucinations Leaderboard results 4 86% ▇▇▇▇▇▇▇▇▇▇
Vectara hallucination leaderboard 1 10% ▇
prior: pull toward the middle 4% ▇
weighted average = 34.5
Licences: Hallucinations Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## 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 Orca 2 13B (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Orca-2-13B-GGUF/tree/a81534ca9ad14dd5e47eadeb04976528cd639e5f))
```sh
llama cli -hf TheBloke/Orca-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/Orca-2-13B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~13.8 GB) `llama cli -hf TheBloke/Orca-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 Orca 2 13B (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Orca-2-13B-GGUF/tree/a81534ca9ad14dd5e47eadeb04976528cd639e5f))
```sh
lms get https://huggingface.co/TheBloke/Orca-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/Orca-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 Orca 2 13B (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Orca-2-13B-GGUF/tree/a81534ca9ad14dd5e47eadeb04976528cd639e5f))
```sh
ollama run hf.co/TheBloke/Orca-2-13B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~13.8 GB) `ollama run hf.co/TheBloke/Orca-2-13B-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (fp32, needs ~52.06 GB, from [huggingface.co/microsoft](https://huggingface.co/microsoft/Orca-2-13b/tree/2539ff53e6baa4cc603774ad5a2d646f4041ea4e))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='microsoft/Orca-2-13b', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/microsoft](https://huggingface.co/microsoft/Orca-2-13b)
[interactive version](https://whatmodel.sh/model/orca-2-13b) · [JSON](https://whatmodel.sh/api?format=json&all=1)