[whatmodel.sh](../../README.md) / Phi-3-mini-4k-instruct (June 2024 update)
# Phi-3-mini-4k-instruct (June 2024 update)
Microsoft · released 2024-06 · 3.82B params · mit
**score 45.7** ±0.9 · #458 of 732 · from 7 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 Phi-3-mini-4k-instruct (June 2024 update) among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #333 of 469* 23% 1
chat & writing ▁▁▁●▆▄▆█▆▇▇▃ #363 of 402* 8.2% 1
math ▁▁▃●█▆▄█▄▂▁▁ #377 of 431* 22% 2
coding · #333 of 469 · 23% of its score
Beats [Qwen3-VL 8B Instruct](qwen3-vl-8b-instruct.md), [Qwen3-VL 4B Instruct](qwen3-vl-4b-instruct.md), [LFM2.5 2.6B](lfm2-5-2-6b.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Llama 3.2 11B Vision Instruct](llama-3-2-11b-vision.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CRUXEval (ZeroEval, 800 problems) 44.8% ▁▂●▃▆█▆▃▂·▁▁ beats 8/47 medium · avg 57% 23% sharp · at its level
chat & writing · #363 of 402 · 8.2% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.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
LMArena (text) 1143.2 ▁▁▁●▃▃▃▆▆▇█▃ beats 35/385 avg 1260 8.2% blunt
math · #377 of 431 · 22% of its score
Beats [Command R Plus (04-2024)](command-r-plus-04-2024.md) here, though they rank higher overall. Trails [GPT-Neo 1.3B](gpt-neo-1-3b.md), [OPT-1.3B](opt-1-3b.md), [OPT-2.7B](opt-2-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (ZeroEval, zero-shot) 75.5% ▁▄·▁●█▅▅▅███ beats 6/43 very easy · avg 87% 15% sharp
MATH Level 5 (ZeroEval, 721 probl… 16.2% ▁▁▁●█▂▇▇▄··▂ beats 4/38 hard · avg 30% 6.9% sharp
other tests (3)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 70.3% ▁·▁▁▄●▄▅█▄▂▁ beats 15/53 easy · avg 75% 40% at its level
ZebraLogic (ZeroEval, 1,000 puzzl… 11.6% ▁▁▃●██▁▁·▁▁▁ beats 12/57 hard · avg 15% 6.2% sharp
EuroEval Life in the UK (English … 80.2% ▁▁▁▁▁▁▁▂▄●▄▃ beats 67/146 easy · avg 82% 1.2% at its level · noisy
source results share of the score
WildEval ZeroEval published results 5 90% ▇▇▇▇▇▇▇▇▇▇▇
LMArena leaderboard dataset 1 8% ▇
EuroEval leaderboards 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 45.7
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · 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 Phi-3-mini-4k-instruct (June 2024 update) (Q4_K_M, needs ~2.39 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Phi-3.1-mini-4k-instruct-GGUF/tree/66a614ace4d069a12c2f6043f4ea92621c898d4a))
```sh
llama cli -hf bartowski/Phi-3.1-mini-4k-instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/Phi-3.1-mini-4k-instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~4.06 GB) `llama cli -hf bartowski/Phi-3.1-mini-4k-instruct-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 Phi-3-mini-4k-instruct (June 2024 update) (Q4_K_M, needs ~2.39 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Phi-3.1-mini-4k-instruct-GGUF/tree/66a614ace4d069a12c2f6043f4ea92621c898d4a))
```sh
lms get https://huggingface.co/bartowski/Phi-3.1-mini-4k-instruct-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 (~4.06 GB) `lms get https://huggingface.co/bartowski/Phi-3.1-mini-4k-instruct-GGUF@Q8_0 -y` · 4bit (~2.1 GB, Mac only) `lms get https://huggingface.co/mlx-community/Phi-3-mini-4k-instruct-4bit -y` · 8bit (~4.1 GB, Mac only) `lms get https://huggingface.co/mlx-community/Phi-3-mini-4k-instruct-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 Phi-3-mini-4k-instruct (June 2024 update) (Q4_K_M, needs ~2.39 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Phi-3.1-mini-4k-instruct-GGUF/tree/66a614ace4d069a12c2f6043f4ea92621c898d4a))
```sh
ollama run hf.co/bartowski/Phi-3.1-mini-4k-instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~4.06 GB) `ollama run hf.co/bartowski/Phi-3.1-mini-4k-instruct-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 Phi-3-mini-4k-instruct (June 2024 update) (4bit, needs ~2.1 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Phi-3-mini-4k-instruct-4bit/tree/5b3819ed6317784fb20eddeae9bed984f778d0d0))
```sh
mlx_lm.chat --model mlx-community/Phi-3-mini-4k-instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Phi-3-mini-4k-instruct-4bit
```
Other versions: 8bit (~4.1 GB, Mac only) `mlx_lm.chat --model mlx-community/Phi-3-mini-4k-instruct-8bit`
Model repository and documentation: [huggingface.co/microsoft](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
[interactive version](https://whatmodel.sh/model/phi-3-mini-4k-instruct-june-2024) · [JSON](https://whatmodel.sh/api?format=json&all=1)