[whatmodel.sh](../../README.md) / Phi-1.5 (1.3B)
# Phi-1.5 (1.3B)
Microsoft · released 2023-09 · 1.42B params · mit
**score 32.6** ±0.8 · #647 of 732 · from 22 benchmark results across 4 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Phi-1.5 (1.3B) among every model on that task or test, worst to best.
other tests (14)
test result vs the others in its run difficulty counts why
ARC Challenge 48.0% ▁·▁··▁▁▇▂█●▃ beats 23/26 easy · avg 82% 45% sharp · at its level · several runs
WinoGrande 72.5% ▁····▁··▁▂█● beats 25/26 easy · avg 86% 11% at its level · several runs
BoolQ 74.5% ▂▃▂▅▆▂▅▆●▂▂▂ beats 20/26 easy · avg 88% 8.3% at its level · several runs
HellaSwag 62.6% ▃···▁●▅█▇▃▁▁ beats 6/26 very easy · avg 91% 6.3% at its level · several runs
ARC Easy 73.1% ▁▁·▁▁▄▅▄█●▁▃ beats 23/26 easy · avg 85% 5.3% blunt · at its level · several runs
OpenBookQA 37.6% █·▁▁▃·▁▃▅▇▃● beats 26/26 hard · avg 49% 5.2% several runs
MedQA (USMLE, 4 options) 31.1% ▁▁··▄▁●··▄▃█ beats 6/17 medium · avg 71% 3.9% sharp
PIQA 76.3% ▁·▁··▁▁▃●▅█▂ beats 12/26 easy · avg 87% 3.9% blunt · at its level · several runs
Social IQA 55.4% ▁···▁▃█▁▁▁·● beats 26/26 medium · avg 60% 3.3% blunt · several runs
SciQ 93.2% ▁·▁·▁·▃▅▃●▄▁ beats 16/26 very easy · avg 97% 2.6% blunt
MedMCQA 34.3% ▂▂··▄▄●▂·▄██ beats 6/17 medium · avg 63% 2.0% typical
LAMBADA (OpenAI version), last-wo… 52.7% ▁··●▁▁▁▂▆█▅▃ beats 2/26 very easy · avg 86% 1.5% blunt · at its level
Open PT-LLM Leaderboard average (… 29.6% ●▁▁▁▁▁▁▁▂█▅▁ beats 11/276 medium · avg 72% 1.3% noisy
PubMedQA 67.8% ▂·▂▆·▄▂●▄▂█▂ beats 8/17 medium · avg 75% 0.3% blunt · at its level
source results share of the score
Stability AI 10 67% ▇▇▇▇▇▇▇▇
Textbooks Are All You Need II: phi-1.5 … 8 25% ▇▇▇
Open Medical-LLM Leaderboard results 3 6% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 32.6
Licences: Stability AI ([Apache-2.0](../../licenses/stablelm-original-eval-json-Apache-2.0.txt)) · Textbooks Are All You Need II: phi-1.5 technical report ([CC BY 4.0](../../licenses/paper-2309.05463v1-CC-BY-4.0.txt)) · Open Medical-LLM Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · 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-1.5 (1.3B) (Q4_K_M, needs ~0.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/phi-1_5-GGUF/tree/fb3f2464a557228caf113b4d6ca7bebb2dc6c08c))
```sh
llama cli -hf mradermacher/phi-1_5-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/phi-1_5-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.5 GB) `llama cli -hf mradermacher/phi-1_5-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-1.5 (1.3B) (Q4_K_M, needs ~0.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/phi-1_5-GGUF/tree/fb3f2464a557228caf113b4d6ca7bebb2dc6c08c))
```sh
lms get https://huggingface.co/mradermacher/phi-1_5-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 (~1.5 GB) `lms get https://huggingface.co/mradermacher/phi-1_5-GGUF@Q8_0 -y` · fp16 (~2.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/phi-1_5 -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-1.5 (1.3B) (Q4_K_M, needs ~0.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/phi-1_5-GGUF/tree/fb3f2464a557228caf113b4d6ca7bebb2dc6c08c))
```sh
ollama run hf.co/mradermacher/phi-1_5-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.5 GB) `ollama run hf.co/mradermacher/phi-1_5-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-1.5 (1.3B) (fp16, needs ~2.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/phi-1_5/tree/38df47b909f79aa418b2d58cb2a6a457f3a8ec83))
```sh
mlx_lm.chat --model mlx-community/phi-1_5
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/phi-1_5
```
### Other ways to run it
1. Run it as published (fp16, needs ~2.84 GB, from [huggingface.co/microsoft](https://huggingface.co/microsoft/phi-1_5/tree/77aa61eeac94fbf33d492b9f2744c98b42d5b5eb))
```sh
uv run --with torch --with 'transformers<5' python -c "from transformers import pipeline; print(pipeline('text-generation', model='microsoft/phi-1_5')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/microsoft](https://huggingface.co/microsoft/phi-1_5)
[interactive version](https://whatmodel.sh/model/phi-1-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)