[whatmodel.sh](../../README.md) / Mistral 7B v0.1 (base)
# Mistral 7B v0.1 (base)
Mistral AI · released 2023-09 · 7.24B params · apache-2.0
**score 37.3** ±0.5 · #604 of 732 · from 59 benchmark results across 10 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Mistral 7B v0.1 (base) among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁●▅▇█▇▅▄▃▁ #433 of 469 40% 4
math ▁▁▃▅●▆▄█▄▂▁▁ #282 of 431 8.7% 2
coding · #433 of 469 · 40% of its score
Beats [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [DeepSeek R1 Distill Llama 8B](deepseek-r1-distill-llama-8b.md) here, though they rank higher overall. Trails [Llama 3.2 1B Instruct](llama-3-2-1b.md), [SmolLM2 1.7B Instruct](smollm2-1-7b.md), [Mistral 7B Instruct v0.1](mistral-7b-instruct-v0-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 28.7% ▁·▁▁●▄▆▅█▃▁▁ beats 11/99 easy · avg 76% 22% sharp · at its level · several runs
HumanEval+ 23.8% ▁·▁▁▁●▄▄▄█▃▁ beats 12/99 easy · avg 70% 12% sharp · at its level
MBPP (maker) 50.2% ▄··▄·▄··●··▄ beats 4/5 easy · avg 69% 3.9% at its level · several runs
MBPP+ 42.1% ▁···▁▁●▅▇█▆▂ beats 8/75 easy · avg 66% 2.5% at its level
math · #282 of 431 · 8.7% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [Llama 3.3 70B Instruct](llama-3-3-70b.md), [DeepSeek-V2.5](deepseek-v2-5.md) here, though they rank higher overall. Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Gemma 7B (base)](gemma-7b-base.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 50.0% █···█·█●··██ beats 3/5 easy · avg 82% 6.9% sharp · at its level · several runs
MATH (maker) 12.7% █·█·█·●█···█ beats 3/5 hard · avg 27% 1.9% several runs
other tests (23)
test result vs the others in its run difficulty counts why
ARC Challenge 54.4% ▁·▁··▁▁▇▂█▆● beats 26/26 easy · avg 82% 17% sharp · at its level · several runs
MMLU (maker) 62.5% ▄····▄▄·●··█ beats 3/5 easy · avg 82% 4.5% at its level · several runs
WinoGrande 74.0% ▁····▁··▁▂█● beats 26/26 easy · avg 86% 4.2% at its level · several runs
BIG-Bench Hard 41.1% ▁▁▁▁▂▄▅●█▃▃▁ beats 78/155 medium · avg 65% 4.1% sharp · several runs
Natural Questions (closed book) 30.1% ▁·▁·▁▂▁▂·▅▂● beats 26/28 medium · avg 48% 3.5% at its level · several runs
HellaSwag 81.1% ▃···▁▇▅█▇●▁▁ beats 24/26 very easy · avg 91% 2.3% at its level · several runs
BoolQ 83.6% ▂▃▂▅▆▂▅▆█▂▂● beats 26/26 easy · avg 88% 2.0% at its level · several runs
TriviaQA 70.9% ▁··▁█▃▃▁▄▅▄● beats 28/28 easy · avg 83% 1.8% at its level · several runs
ARC Easy 79.5% ▁▁·▁▁▄▅▄█▅▁● beats 26/26 easy · avg 85% 1.7% blunt · at its level · several runs
CommonsenseQA 66.3% █·····▂▂▃▂●▄ beats 30/38 very easy · avg 94% 1.6% sharp · at its level · small run
PIQA 80.8% ▁·▁··▁▁▃█▅█● beats 26/26 easy · avg 87% 1.3% blunt · at its level · several runs
OpenBookQA 32.8% █·▁▁▃·▁▃▅▇●▄ beats 22/26 hard · avg 49% 1.2% typical
Factual knowledge recall, Premium… 28.4% ▅····▃▅·█●·█ beats 10/13 medium · avg 52% 1.2% at its level
Social IQA 42.7% ▁···▁▃●▁▁▁·▁ beats 19/26 hard · avg 56% 0.8% blunt · several runs
Factual knowledge recall, Premium… 40.8% ▃▅▃··▅▃·●▃▃▅ beats 7/13 medium · avg 61% 0.6% at its level
SciQ 95.9% ▁·▁·▁·▃▅▃█▄● beats 26/26 very easy · avg 97% 0.6% blunt · maxed out
AGIEval English (Gemma report) 41.2% ▄··········● beats 1/2 medium · avg 62% 0.5% at its level · small run
Open PT-LLM Leaderboard average (… 61.1% ▃▁▁▁▁▁▁▁●█▅▁ beats 92/276 medium · avg 72% 0.5% at its level · noisy
C-Eval 46.1% ▁···▁▁●▁▂▂▃█ beats 6/33 medium · avg 59% 0.4% noisy
LAMBADA (OpenAI version), last-wo… 75.7% ▁··▂▁▁▁▂▆█▅● beats 25/26 very easy · avg 86% 0.4% blunt
EuroEval Life in the UK (English … 76.3% ▁▁▁▁▁▁▁▂●█▄▃ beats 53/146 easy · avg 82% 0.4% at its level · noisy
HaluEval QA hallucination detecti… 54.5% █▁▁·▁▁·●▁▁▁▁ beats 21/27 hard · avg 66% 0.2% noisy
TruthfulQA MC2 42.6% ▅▃●▆▁▂▅···▁▁ beats 15/31 medium · avg 57% 0.1% blunt · at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 30% ▇▇▇▇
Stability AI 10 22% ▇▇▇
Mixtral of Experts (submitted / maker-r… 12 16% ▇▇
MiniCPM: Unveiling the Potential of Sma… 7 11% ▇
Mistral 7B (submitted / maker-reported) 12 9% ▇
Gemma report 7 7% ▇
Hallucinations Leaderboard results 4 2% ▇
Towards a Holistic Evaluation of LLMs o… 2 2% ▇
Open Portuguese LLM Leaderboard 1 0% ▇
EuroEval leaderboards 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 37.3
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · Stability AI ([Apache-2.0](../../licenses/stablelm-original-eval-json-Apache-2.0.txt)) · Mixtral of Experts ([CC BY 4.0](../../licenses/CC-BY-4.0.txt)) · MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Mistral 7B ([CC BY 4.0](../../licenses/paper-2310.06825v1-CC-BY-4.0.txt)) · Gemma report ([CC BY 4.0](../../licenses/CC-BY-4.0.txt)) · Hallucinations Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Towards a Holistic Evaluation of LLMs on Factual Knowledge Recall ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE))
## 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 Mistral 7B v0.1 (base) (Q4_K_M, needs ~4.4 GB, from [huggingface.co/MaziyarPanahi](https://huggingface.co/MaziyarPanahi/Mistral-7B-v0.1-GGUF/tree/4b85242ce137d0835f77bf48f32d1f93494c402b))
```sh
llama cli -hf MaziyarPanahi/Mistral-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 MaziyarPanahi/Mistral-7B-v0.1-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `llama cli -hf MaziyarPanahi/Mistral-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 Mistral 7B v0.1 (base) (Q4_K_M, needs ~4.4 GB, from [huggingface.co/MaziyarPanahi](https://huggingface.co/MaziyarPanahi/Mistral-7B-v0.1-GGUF/tree/4b85242ce137d0835f77bf48f32d1f93494c402b))
```sh
lms get https://huggingface.co/MaziyarPanahi/Mistral-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/MaziyarPanahi/Mistral-7B-v0.1-GGUF@Q8_0 -y` · 4bit (~4.1 GB, Mac only) `lms get https://huggingface.co/en3rypt/Mistral-7B-v0.1-4bit -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 Mistral 7B v0.1 (base) (Q4_K_M, needs ~4.4 GB, from [huggingface.co/MaziyarPanahi](https://huggingface.co/MaziyarPanahi/Mistral-7B-v0.1-GGUF/tree/4b85242ce137d0835f77bf48f32d1f93494c402b))
```sh
ollama run hf.co/MaziyarPanahi/Mistral-7B-v0.1-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `ollama run hf.co/MaziyarPanahi/Mistral-7B-v0.1-GGUF: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 Mistral 7B v0.1 (base) (4bit, needs ~4.1 GB, Mac only, from [huggingface.co/en3rypt](https://huggingface.co/en3rypt/Mistral-7B-v0.1-4bit/tree/5c072ca42a3856c6f042840a7e9dcad5e074d69f))
```sh
mlx_lm.chat --model en3rypt/Mistral-7B-v0.1-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model en3rypt/Mistral-7B-v0.1-4bit
```
### Other ways to run it
1. Run it as published (BF16, needs ~14.48 GB, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Mistral-7B-v0.1/tree/27d67f1b5f57dc0953326b2601d68371d40ea8da))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='mistralai/Mistral-7B-v0.1', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Mistral-7B-v0.1)
[interactive version](https://whatmodel.sh/model/mistral-7b-v0-1) · [JSON](https://whatmodel.sh/api?format=json&all=1)