[whatmodel.sh](../../README.md) / Dolphin 2.2.1 Mistral 7B
# Dolphin 2.2.1 Mistral 7B
Cognitive Computations · released 2023-10 · 7.24B params · apache-2.0
**score 38.8** ±1.6 · #580 of 732 · from 3 benchmark results across 3 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Dolphin 2.2.1 Mistral 7B among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #358 of 402 99% 3
chat & writing · #358 of 402 · 99% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [Phi-3-mini-4k-instruct (June 2024 update)](phi-3-mini-4k-instruct-june-2024.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 H 350M](granite-4-0-h-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 13.1% ▁▂▂▃●▆█▆▄▄▁▁ beats 31/122 medium · avg 35% 51% sharp
LMArena (text) 1152.3 ▁▁▁▂●▃▃▆▆▇█▃ beats 41/385 avg 1260 25% blunt
Polyrating rating (bias-aware re-… 1060.0 ▂▁▂▁▃●█▆▇▄▂▂ beats 30/95 avg 1187 23% typical
source results share of the score
AlpacaEval 1.0 and 2.0 leaderboards 1 51% ▇▇▇▇▇▇
LMArena leaderboard dataset 1 25% ▇▇▇
Polyrating: A Cost-Effective and Bias-A… 1 23% ▇▇▇
prior: pull toward the middle 1% ▇
weighted average = 38.8
Licences: AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · 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 Dolphin 2.2.1 Mistral 7B (Q4_K_M, needs ~4.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/dolphin-2.2.1-mistral-7B-GGUF/tree/34548d6105f2ffdc35b066364a954d42c24a2d87))
```sh
llama cli -hf TheBloke/dolphin-2.2.1-mistral-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/dolphin-2.2.1-mistral-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `llama cli -hf TheBloke/dolphin-2.2.1-mistral-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 Dolphin 2.2.1 Mistral 7B (Q4_K_M, needs ~4.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/dolphin-2.2.1-mistral-7B-GGUF/tree/34548d6105f2ffdc35b066364a954d42c24a2d87))
```sh
lms get https://huggingface.co/TheBloke/dolphin-2.2.1-mistral-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 (~7.7 GB) `lms get https://huggingface.co/TheBloke/dolphin-2.2.1-mistral-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 Dolphin 2.2.1 Mistral 7B (Q4_K_M, needs ~4.84 GB, from [ollama.com](https://ollama.com/library/dolphin-mistral/tags))
```sh
ollama run dolphin-mistral:7b-v2.2.1-q4_K_M
```
Other versions: Q8_0 (~9.19 GB) `ollama run dolphin-mistral:7b-v2.2.1-q8_0`
Model repository and documentation: [huggingface.co/dphn](https://huggingface.co/dphn/dolphin-2.2.1-mistral-7b)
[interactive version](https://whatmodel.sh/model/dolphin-2-2-1-mistral-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)