[whatmodel.sh](../../README.md) / Mistral 7B Instruct v0.1
# Mistral 7B Instruct v0.1
Mistral · released 2023-09 · 7.24B params · apache-2.0
**score 34.0** ±1.0 · #637 of 732 · from 13 benchmark results across 9 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 Instruct v0.1 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁●▅▇█▇▅▄▃▁ #429 of 469* 36% 1
chat & writing ▁▁▁●▆▄▆█▆▇▇▃ #385 of 402* 19% 2
coding · #429 of 469 · 36% 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) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 28.7% ▁●▄▅▇█·▄▇▅▄▄ beats 3/42 easy · avg 75% 36% sharp · at its level
chat & writing · #385 of 402 · 19% of its score
Beats [Phi-2 (2.7B)](phi-2.md), [Nous-Hermes-13b](nous-hermes-13b.md), [Qwen1.5 4B Chat](qwen1-5-4b-chat.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 350M](granite-4-0-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1110.4 ▁▁▁●▃▃▃▆▆▇█▃ beats 17/385 avg 1260 10% blunt
Polyrating rating (bias-aware re-… 1008.0 ▂▁▂▁●▇█▆▇▄▂▂ beats 16/95 avg 1187 9.0% typical
other tests (10)
test result vs the others in its run difficulty counts why
EvoEval (overall) 17.6% ▂●▃▄▄█▁▄▅▃▁▃ beats 2/42 medium · avg 52% 18% typical
MMLU-Pro 24.2% ●▂▅█▃·█▃▅··▂ beats 0/23 medium · avg 54% 10% typical
Natural Questions (closed book) 17.8% ▁·▁·▁▂▁▂·●▂█ beats 14/28 medium · avg 48% 8.1% typical
Open Persian LLM Leaderboard v2 a… 36.5% ▂●▂▃█▄▃▆▃▅▂▂ beats 8/81 medium · avg 57% 4.3% blunt · at its level
Open PT-LLM Leaderboard average (… 59.7% ▃▁▁▁▁▁▁▁●█▅▁ beats 88/276 medium · avg 72% 1.8% at its level · noisy
EuroEval Life in the UK (English … 69.0% ▁▁▁▁▁▁▁▂●█▄▃ beats 37/146 easy · avg 82% 1.6% at its level · noisy
TriviaQA 52.2% ▁··▁█▃▃▁▄▅●█ beats 20/28 easy · avg 73% 1.1% at its level · noisy
HaluEval QA hallucination detecti… 49.2% ●▁▁·▁▁·▁▁▁▁▁ beats 4/27 hard · avg 66% 0.6% noisy
TruthfulQA MC2 55.9% ▅▃█▆▁▂●···▁▁ beats 28/31 medium · avg 57% 0.3% blunt · at its level
FANToM BeliefQ [Choice], short co… 27.6% ●·····▂····▁ beats 5/11 very hard · avg 55% 0.2% sharp · too hard for it · noisy
source results share of the score
Top Leaderboard Ranking = Top Coding Pr… 2 53% ▇▇▇▇▇▇
Hallucinations Leaderboard results 4 10% ▇
LMArena leaderboard dataset 1 10% ▇
MMLU-Pro author-released evaluation runs 1 10% ▇
Polyrating: A Cost-Effective and Bias-A… 1 9% ▇
Open Persian LLM Leaderboard v2 1 4% ▇
Open Portuguese LLM Leaderboard 1 2% ▇
EuroEval leaderboards 1 2% ▇
FANToM: A Benchmark for Stress-testing … 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 34.0
Licences: Top Leaderboard Ranking = Top Coding Proficiency ([CC BY 4.0](../../licenses/paper-2403.19114v1-CC-BY-4.0.txt)) · Hallucinations Leaderboard results ([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/)) · MMLU-Pro author-released evaluation runs ([Apache-2.0](../../licenses/mmlu-pro-author-runs-Apache-2.0.txt)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · 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)) · FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-890-CC-BY-4-0.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 Mistral 7B Instruct v0.1 (Q4_K_M, needs ~4.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/tree/731a9fc8f06f5f5e2db8a0cf9d256197eb6e05d1))
```sh
llama cli -hf TheBloke/Mistral-7B-Instruct-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 TheBloke/Mistral-7B-Instruct-v0.1-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `llama cli -hf TheBloke/Mistral-7B-Instruct-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 Instruct v0.1 (Q4_K_M, needs ~4.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/tree/731a9fc8f06f5f5e2db8a0cf9d256197eb6e05d1))
```sh
lms get https://huggingface.co/TheBloke/Mistral-7B-Instruct-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/TheBloke/Mistral-7B-Instruct-v0.1-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 Mistral 7B Instruct v0.1 (Q4_0, needs ~4.84 GB, from [ollama.com](https://ollama.com/library/mistral/tags))
```sh
ollama run mistral:v0.1
```
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
[interactive version](https://whatmodel.sh/model/mistral-7b-instruct-v0-1) · [JSON](https://whatmodel.sh/api?format=json&all=1)