[whatmodel.sh](../../README.md) / Phi-3 Medium 128K Instruct
# Phi-3 Medium 128K Instruct
Microsoft · released 2024-04 · 13.96B params · mit
**score 46.6** ±1.2 · #443 of 732 · from 10 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 Medium 128K Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #266 of 469 12% 4
math ▁▁▃●█▆▄█▄▂▁▁ #355 of 431* 17% 1
coding · #266 of 469 · 12% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CodeReviewQA: solution identifica… 50.8% ▃··▁▂▂▃▂●▃▄▂ beats 26/47 medium · avg 51% 4.3% sharp · at its level · noisy
CodeReviewQA: change localisation… 43.0% █▂▁▁▂▂▂▃▅●▅▇ beats 32/47 hard · avg 33% 3.9% sharp · noisy
CodeReviewQA: automated code refi… 30.2% ▁▁▁▁▁▁▂▂▃●▅▄ beats 20/47 hard · avg 32% 3.3% at its level
CodeReviewQA: change type recogni… 76.7% ▁▁▁······▁▃● beats 37/47 easy · avg 67% 0.1% blunt · at its level · noisy
math · #355 of 431 · 17% of its score
Beats [GPT-3.5 Turbo (0125)](gpt-3-5-turbo-0125.md), [DBRX Instruct](dbrx-instruct.md) here, though they rank higher overall. Trails [OPT-2.7B](opt-2-7b.md), [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 17.6% ▂▂▅●▄█▆▃▅▂▃▄ beats 15/92 hard · avg 28% 17% sharp
other tests (5)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 30.3% ▁▁▁▂▂▃▂●▆▃▅▃ beats 28/61 medium · avg 38% 53% at its level
GPQA Diamond 27.6% ▁▁●▁▂▄▆▄▅▇█▅ beats 12/211 hard · avg 39% 10% sharp
RULER 64k 78.6% ▁▁·▁·▂●▄█▂·▁ beats 5/22 easy · avg 82% 3.4% typical
Open PT-LLM Leaderboard average (… 71.7% ▃▁▁▁▁▁▁▁▂●▅▁ beats 188/276 medium · avg 72% 3.4% at its level · noisy
RULER 128k 46.1% ▄··▂●▂▆██▄▄▂ beats 3/21 easy · avg 67% 1.1% at its level · noisy
source results share of the score
LiveBench model judgments 1 53% ▇▇▇▇▇▇
Epoch AI 2 27% ▇▇▇
CodeReviewQA: The Code Review Comprehen… 4 12% ▇
NVIDIA RULER repository README results … 2 5% ▇
Open Portuguese LLM Leaderboard 1 3% ▇
prior: pull toward the middle 1% ▇
weighted average = 46.6
Licences: LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · NVIDIA RULER repository README results table ([Apache-2.0](https://github.com/NVIDIA/RULER/blob/c3f5e3b4f87f97e048793bb510a3a6b19a46bf3a/LICENSE)) · 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 Medium 128K Instruct (Q4_K_M, needs ~8.6 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/tree/950c8154e91f33193217eebf1c26903af44c6e13))
```sh
llama cli -hf bartowski/Phi-3-medium-128k-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-medium-128k-instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~14.8 GB) `llama cli -hf bartowski/Phi-3-medium-128k-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 Medium 128K Instruct (Q4_K_M, needs ~8.6 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/tree/950c8154e91f33193217eebf1c26903af44c6e13))
```sh
lms get https://huggingface.co/bartowski/Phi-3-medium-128k-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 (~14.8 GB) `lms get https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF@Q8_0 -y` · 4bit (~7.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Phi-3-medium-128k-instruct-4bit -y` · 8bit (~14.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/Phi-3-medium-128k-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 Medium 128K Instruct (Q4_0, needs ~8.88 GB, from [ollama.com](https://ollama.com/library/phi3/tags))
```sh
ollama run phi3:medium-128k
```
Other versions: Q8_0 (~17.25 GB) `ollama run phi3:14b-medium-128k-instruct-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 Medium 128K Instruct (4bit, needs ~7.9 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Phi-3-medium-128k-instruct-4bit/tree/9cffe737ae8c598cf3fca728b8417132754043f8))
```sh
mlx_lm.chat --model mlx-community/Phi-3-medium-128k-instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Phi-3-medium-128k-instruct-4bit
```
Other versions: 8bit (~14.8 GB, Mac only) `mlx_lm.chat --model mlx-community/Phi-3-medium-128k-instruct-8bit`
Model repository and documentation: [huggingface.co/microsoft](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct)
[interactive version](https://whatmodel.sh/model/phi-3-medium-128k-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)