[whatmodel.sh](../../README.md) / Dolphin 2.6 Mixtral 8x7B
# Dolphin 2.6 Mixtral 8x7B
Cognitive Computations · released 2023-12 · apache-2.0
**score 45.4** ±0.8 · #466 of 732 · from 3 benchmark results across 1 source
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Dolphin 2.6 Mixtral 8x7B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #345 of 469 100% 3
coding · #345 of 469 · 100% of its score
Beats [LFM2.5 2.6B](lfm2-5-2-6b.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md) here, though they rank higher overall. Trails [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 64.0% ▁·▁▁▃▄▆●█▃▁▁ beats 48/99 easy · avg 76% 50% sharp · at its level
HumanEval+ 57.3% ▁·▁▁▁▃▄▄●█▃▁ beats 49/99 easy · avg 70% 42% sharp · at its level
MBPP+ 59.0% ▁···▁▁▂▅●█▆▂ beats 35/75 easy · avg 66% 8.0% at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 100% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 0% ▇
weighted average = 45.4
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-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 Dolphin 2.6 Mixtral 8x7B (Q4_K_M, needs ~26.4 GB, from [huggingface.co/dphn](https://huggingface.co/dphn/dolphin-2.6-mixtral-8x7b-GGUF/tree/e4ef1c9c5213bead613491a34103c5b35705ee89))
```sh
llama cli -hf dphn/dolphin-2.6-mixtral-8x7b-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf dphn/dolphin-2.6-mixtral-8x7b-GGUF:Q4_K_M
```
### 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 2.6 Mixtral 8x7B (Q4_K_M, needs ~26.4 GB, from [huggingface.co/dphn](https://huggingface.co/dphn/dolphin-2.6-mixtral-8x7b-GGUF/tree/e4ef1c9c5213bead613491a34103c5b35705ee89))
```sh
lms get https://huggingface.co/dphn/dolphin-2.6-mixtral-8x7b-GGUF@Q4_K_M -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 Dolphin 2.6 Mixtral 8x7B (Q4_K_M, needs ~26.4 GB, from [huggingface.co/dphn](https://huggingface.co/dphn/dolphin-2.6-mixtral-8x7b-GGUF/tree/e4ef1c9c5213bead613491a34103c5b35705ee89))
```sh
ollama run hf.co/dphn/dolphin-2.6-mixtral-8x7b-GGUF:Q4_K_M
```
### Other ways to run it
1. Run it as published (BF16, needs ~93.41 GB, from [huggingface.co/dphn](https://huggingface.co/dphn/dolphin-2.6-mixtral-8x7b/tree/db797db3f23f58b79f9815222b83c1a6f7bb3f25))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='dphn/dolphin-2.6-mixtral-8x7b', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/dphn](https://huggingface.co/dphn/dolphin-2.6-mixtral-8x7b)
[interactive version](https://whatmodel.sh/model/dolphin-2-6-mixtral-8x7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)