[whatmodel.sh](../../README.md) / Qwen1.5 0.5B Chat
# Qwen1.5 0.5B Chat
Alibaba · released 2024-01 · 0.62B params · tongyi-qianwen-research
**score 23.1** ±3.0 · #694 of 732 · from 3 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 Qwen1.5 0.5B Chat among every model on that task or test, worst to best.
other tests (3)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 5.2% ●▁▁▂▂▃▂█▆▃▅▃ beats 0/61 medium · avg 38% 77% too hard for it
EuroEval Life in the UK (English … 37.4% ▁▁▁▁▁●▁▂▄█▄▃ beats 12/146 easy · avg 82% 10% noisy
Open PT-LLM Leaderboard average (… 25.4% ●▁▁▁▁▁▁▁▂█▅▁ beats 30/276 medium · avg 72% 10% noisy
source results share of the score
LiveBench model judgments 1 77% ▇▇▇▇▇▇▇▇▇
EuroEval leaderboards 1 10% ▇
Open Portuguese LLM Leaderboard 1 10% ▇
prior: pull toward the middle 4% ▇
weighted average = 23.1
Licences: LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/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 Qwen1.5 0.5B Chat (Q4_K_M, needs ~0.41 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat-GGUF/tree/cfab082d2fef4a8736ef384dc764c2fb6887f387))
```sh
llama cli -hf Qwen/Qwen1.5-0.5B-Chat-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf Qwen/Qwen1.5-0.5B-Chat-GGUF:Q4_K_M
```
### 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 Qwen1.5 0.5B Chat (Q4_K_M, needs ~0.41 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat-GGUF/tree/cfab082d2fef4a8736ef384dc764c2fb6887f387))
```sh
lms get https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: 4bit (~0.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen1.5-0.5B-Chat-4bit -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 Qwen1.5 0.5B Chat (Q4_0, needs ~0.395 GB, from [ollama.com](https://ollama.com/library/qwen/tags))
```sh
ollama run qwen:0.5b
```
### 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 Qwen1.5 0.5B Chat (4bit, needs ~0.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen1.5-0.5B-Chat-4bit/tree/659d8dafc39202a6688bb46242d60440702489b1))
```sh
mlx_lm.chat --model mlx-community/Qwen1.5-0.5B-Chat-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen1.5-0.5B-Chat-4bit
```
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat)
[interactive version](https://whatmodel.sh/model/qwen1-5-0-5b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)