[whatmodel.sh](../../README.md) / Qwen1.5 1.8B Chat # Qwen1.5 1.8B Chat Alibaba · released 2024-01 · 1.84B params · tongyi-qianwen-research **score 26.4** ±2.2 · #682 of 732 · from 4 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 Qwen1.5 1.8B Chat among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
chat & writing  ▁●▁▃▆▄▆█▆▇▇▃   #397 of 402*     30%      1
chat & writing · #397 of 402 · 30% of its score Beats [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), [Falcon 7B Instruct](falcon-7b-instruct.md) here, though they rank lower overall. 
test                            result  vs the others in its run  difficulty        counts  why
AlpacaEval 2 length-controlled    2.6%  ●▂▂▃▆▆█▆▄▄▁▁ beats 1/122  medium · avg 35%     30%  sharp · too hard for it
other tests (3) 
test                                result  vs the others in its run   difficulty        counts  why
LiveBench 2024-07-26                  6.0%  ●▁▁▂▂▃▂█▆▃▅▃ beats 1/61    medium · avg 38%     55%  too hard for it
Open PT-LLM Leaderboard average (…   43.2%  ▃▁▁▁▁▁●▁▂█▅▁ beats 53/276  medium · avg 72%    6.5%  noisy
EuroEval Life in the UK (English …   57.2%  ▁▁▁▁▁▁▁●▄█▄▃ beats 28/146  easy · avg 82%      6.3%  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
LiveBench model judgments                  1   55% ▇▇▇▇▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards        1   30% ▇▇▇▇
Open Portuguese LLM Leaderboard            1    6% ▇
EuroEval leaderboards                      1    6% ▇
prior: pull toward the middle                   2% ▇
weighted average                              = 26.4
Licences: LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · AlpacaEval 1.0 and 2.0 leaderboards ([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)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) ## 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 1.8B Chat (Q4_K_M, needs ~1.22 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat-GGUF/tree/c6414b00c3ed52df27201a5386a971869a72496a)) ```sh llama cli -hf Qwen/Qwen1.5-1.8B-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/Qwen1.5-1.8B-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 1.8B Chat (Q4_K_M, needs ~1.22 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat-GGUF/tree/c6414b00c3ed52df27201a5386a971869a72496a)) ```sh lms get https://huggingface.co/Qwen/Qwen1.5-1.8B-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 (~1.5 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen1.5-1.8B-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 1.8B Chat (Q4_0, needs ~1.1 GB, from [ollama.com](https://ollama.com/library/qwen/tags)) ```sh ollama run qwen:1.8b ``` ### 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 1.8B Chat (4bit, needs ~1.5 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen1.5-1.8B-Chat-4bit/tree/7243c8444c29fd6caeadba7506c14d88716c3ecc)) ```sh mlx_lm.chat --model mlx-community/Qwen1.5-1.8B-Chat-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/Qwen1.5-1.8B-Chat-4bit ``` Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat) [interactive version](https://whatmodel.sh/model/qwen1-5-1-8b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)