[whatmodel.sh](../../README.md) / Mathstral 7B v0.1
# Mathstral 7B v0.1
Mistral AI · released 2024-07 · 7.25B params · apache-2.0
**score 43.9** ±1.3 · #504 of 732 · from 4 benchmark results across 4 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Mathstral 7B v0.1 among every model on that task or test, worst to best.
task vs the others rank counts tests
math ▁▁▃●█▆▄█▄▂▁▁ #340 of 431* 4.6% 1
math · #340 of 431 · 4.6% of its score
Beats [Athene-70B](athene-70b.md), [Llama 3 70B Instruct](llama-3-70b.md), [Hermes 2 Theta Llama-3 70B](hermes-2-theta-llama-3-70b.md) here, though they rank higher overall. Trails [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md), [Zephyr 7B beta](zephyr-7b-beta.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH (subset unspecified) (maker) 56.6% ▂▄▄▃●▇▅▆▄▃▄▁ beats 33/80 medium · avg 63% 4.6% at its level · noisy
other tests (3)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 24.5% ▁▁▁▂▂▃●█▆▃▅▃ beats 18/61 medium · avg 38% 58% at its level
MMLU-Pro 42.0% ▃▂▅●▃·█▃▅··▂ beats 9/23 medium · avg 54% 26% at its level
ZebraLogic (ZeroEval, 1,000 puzzl… 9.0% ▁▁●▆██▁▁·▁▁▁ beats 7/57 hard · avg 15% 11% sharp · too hard for it
source results share of the score
LiveBench model judgments 1 58% ▇▇▇▇▇▇▇
MMLU-Pro author-released evaluation runs 1 26% ▇▇▇
WildEval ZeroEval published results 1 11% ▇
Mistral AI vendor-reported benchmark sc… 1 5% ▇
prior: pull toward the middle 1% ▇
weighted average = 43.9
Licences: LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · MMLU-Pro author-released evaluation runs ([Apache-2.0](../../licenses/mmlu-pro-author-runs-Apache-2.0.txt)) · WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-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 Mathstral 7B v0.1 (Q4_K_M, needs ~4.4 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/mathstral-7B-v0.1-GGUF/tree/e16676974b5762ea632eac7ce00ed2419361ee1c))
```sh
llama cli -hf lmstudio-community/mathstral-7B-v0.1-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/mathstral-7B-v0.1-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `llama cli -hf lmstudio-community/mathstral-7B-v0.1-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 Mathstral 7B v0.1 (Q4_K_M, needs ~4.4 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/mathstral-7B-v0.1-GGUF/tree/e16676974b5762ea632eac7ce00ed2419361ee1c))
```sh
lms get https://huggingface.co/lmstudio-community/mathstral-7B-v0.1-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/lmstudio-community/mathstral-7B-v0.1-GGUF@Q8_0 -y` · 4bit (~4.1 GB, Mac only) `lms get https://huggingface.co/mlx-community/mathstral-7B-v0.1-4bit -y` · 8bit (~7.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/mathstral-7B-v0.1-8bit -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 Mathstral 7B v0.1 (Q4_K_M, needs ~4.85 GB, from [ollama.com](https://ollama.com/library/mathstral/tags))
```sh
ollama run mathstral:7b-v0.1-q4_K_M
```
Other versions: Q8_0 (~9.2 GB) `ollama run mathstral:7b-v0.1-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Mathstral 7B v0.1 (4bit, needs ~4.1 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/mathstral-7B-v0.1-4bit/tree/35eb945e90f9e5e32cea43d55ba3c201a861f316))
```sh
mlx_lm.chat --model mlx-community/mathstral-7B-v0.1-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/mathstral-7B-v0.1-4bit
```
Other versions: 8bit (~7.7 GB, Mac only) `mlx_lm.chat --model mlx-community/mathstral-7B-v0.1-8bit`
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Mathstral-7B-v0.1)
[interactive version](https://whatmodel.sh/model/mathstral-7b-v0-1) · [JSON](https://whatmodel.sh/api?format=json&all=1)