[whatmodel.sh](../../README.md) / Phi-3-medium-4k-instruct
# Phi-3-medium-4k-instruct
Microsoft · released 2024-05 · 14B params · mit
**score 46.4** ±1.0 · #447 of 732 · from 7 benchmark results across 7 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-4k-instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆●▆█▆▇▇▃ #311 of 402 44% 3
chat & writing · #311 of 402 · 44% of its score
Beats [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md), [Code Llama 70B Instruct](codellama-70b-instruct.md) here, though they rank higher overall. Trails [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena-Hard (v0.1, GPT-4-1106 judg… 33.4% ▂▁▁▅█●▄▃▃▅▃▁ beats 29/60 medium · avg 47% 23% sharp · at its level
LMArena (text) 1198.0 ▁▁▁▂▃●▃▆▆▇█▃ beats 68/385 avg 1260 11% blunt
Polyrating rating (bias-aware re-… 1126.0 ▂▁▂▁▃▇█●▇▄▂▂ beats 58/95 avg 1187 10% typical
other tests (4)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 31.2% ▁▁▁▂▂▃▂●▆▃▅▃ beats 30/61 medium · avg 38% 39% at its level
EvalPerf correctness (118 tasks, … 68.4% ▂·▂▂▂●▅▆▆▆▃█ beats 5/29 easy · avg 74% 7.2% at its level
Polish EQ-Bench v2 (SpeakLeash) 57.1% ▁··▁▁▂▂▆▅●█▆ beats 34/70 medium · avg 62% 6.7% at its level
Open PT-LLM Leaderboard average (… 72.3% ▃▁▁▁▁▁▁▁▂●▅▁ beats 194/276 medium · avg 72% 2.6% at its level · noisy
source results share of the score
LiveBench model judgments 1 39% ▇▇▇▇▇
Arena-Hard-Auto v0.1 leaderboard 1 23% ▇▇▇
LMArena leaderboard dataset 1 11% ▇
Polyrating: A Cost-Effective and Bias-A… 1 10% ▇
EvalPlus / EvalPerf published evaluatio… 1 7% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 7% ▇
Open Portuguese LLM Leaderboard 1 3% ▇
prior: pull toward the middle 0% ▇
weighted average = 46.4
Licences: LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-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/)) · EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · 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-medium-4k-instruct (Q4_K_M, needs ~8.6 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Phi-3-medium-4k-instruct-GGUF/tree/2e478770bcb73c17668a4f9fa028e8c8228f6fb4))
```sh
llama cli -hf bartowski/Phi-3-medium-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-medium-4k-instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~14.8 GB) `llama cli -hf bartowski/Phi-3-medium-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-medium-4k-instruct (Q4_K_M, needs ~8.6 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Phi-3-medium-4k-instruct-GGUF/tree/2e478770bcb73c17668a4f9fa028e8c8228f6fb4))
```sh
lms get https://huggingface.co/bartowski/Phi-3-medium-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 (~14.8 GB) `lms get https://huggingface.co/bartowski/Phi-3-medium-4k-instruct-GGUF@Q8_0 -y` · 4bit (~7.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Phi-3-medium-4k-instruct-4bit -y` · 8bit (~14.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/Phi-3-medium-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-medium-4k-instruct (Q4_K_M, needs ~8.9 GB, from [ollama.com](https://ollama.com/library/phi3/tags))
```sh
ollama run phi3:14b-medium-4k-instruct-q4_K_M
```
Other versions: Q8_0 (~17.3 GB) `ollama run phi3:14b-medium-4k-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-4k-instruct (4bit, needs ~7.9 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Phi-3-medium-4k-instruct-4bit/tree/c534ae53d267c6395b7a11b790fb2e9860f7f941))
```sh
mlx_lm.chat --model mlx-community/Phi-3-medium-4k-instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Phi-3-medium-4k-instruct-4bit
```
Other versions: 8bit (~14.8 GB, Mac only) `mlx_lm.chat --model mlx-community/Phi-3-medium-4k-instruct-8bit`
Model repository and documentation: [huggingface.co/microsoft](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct)
[interactive version](https://whatmodel.sh/model/phi-3-medium-4k-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)