[whatmodel.sh](../../README.md) / Phi-3-mini-4k-instruct (Apr 2024) # Phi-3-mini-4k-instruct (Apr 2024) Microsoft · released 2024-04 · 3.82B params · mit **score 40.0** ±1.5 · #567 of 732 · from 5 benchmark results across 5 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 (Apr 2024) among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
chat & writing  ▁▁▁●▆▄▆█▆▇▇▃   #373 of 402*     47%      2
chat & writing · #373 of 402 · 47% of its score Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [Phi-2 (2.7B)](phi-2.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)                      1128.2  ▁▁▁●▃▃▃▆▆▇█▃ beats 26/385  avg 1260       24%  blunt
Polyrating rating (bias-aware re-…  1068.0  ▂▁▂▁▃●█▆▇▄▂▂ beats 32/95   avg 1187       22%  typical
other tests (3) 
test                                result  vs the others in its run    difficulty        counts  why
MMLU-Pro                             41.5%  ▃▂▅●▃·█▃▅··▂ beats 7/23     medium · avg 54%     32%  at its level
Polish EQ-Bench v2 (SpeakLeash)      28.1%  ▁··▁▁●▂▆▅▆█▆ beats 7/70     medium · avg 62%     14%  at its level
Open PT-LLM Leaderboard average (…   66.4%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 125/276  medium · avg 72%    5.5%  at its level · noisy
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
MMLU-Pro author-released evaluation runs        1   32% ▇▇▇▇
LMArena leaderboard dataset                     1   24% ▇▇▇
Polyrating: A Cost-Effective and Bias-A…        1   22% ▇▇▇
SpeakLeash Polish EQ-Bench leaderboard          1   14% ▇▇
Open Portuguese LLM Leaderboard                 1    5% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 40.0
Licences: MMLU-Pro author-released evaluation runs ([Apache-2.0](../../licenses/mmlu-pro-author-runs-Apache-2.0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash Polish EQ-Bench leaderboard ([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)) ## 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 (Apr 2024) (Q4_K_M, needs ~2.39 GB, from [huggingface.co/microsoft](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/tree/a64113399c2f6b8ad3e11c394733a2ddadaa7f33)) ```sh llama cli -hf microsoft/Phi-3-mini-4k-instruct-gguf ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf microsoft/Phi-3-mini-4k-instruct-gguf ``` Other versions: fp16 (~7.64 GB) `llama cli -hf microsoft/Phi-3-mini-4k-instruct-gguf` ### 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 (Apr 2024) (Q4_K_M, needs ~2.39 GB, from [huggingface.co/microsoft](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/tree/a64113399c2f6b8ad3e11c394733a2ddadaa7f33)) ```sh lms get https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: fp16 (~7.64 GB) `lms get https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf -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 (Apr 2024) (Q4_K_M, needs ~2.39 GB, from [huggingface.co/microsoft](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/tree/a64113399c2f6b8ad3e11c394733a2ddadaa7f33)) ```sh ollama run hf.co/microsoft/Phi-3-mini-4k-instruct-gguf ``` Other versions: fp16 (~7.64 GB) `ollama run hf.co/microsoft/Phi-3-mini-4k-instruct-gguf` 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) · [JSON](https://whatmodel.sh/api?format=json&all=1)