[whatmodel.sh](../../README.md) / Qwen1.5 4B Chat # Qwen1.5 4B Chat Alibaba · released 2024-02 · 3.95B params · tongyi-qianwen-research **score 34.2** ±1.5 · #635 of 732 · from 5 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 Qwen1.5 4B Chat among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
chat & writing  ▁▁●▃▆▄▆█▆▇▇▃   #389 of 402*     45%      2
chat & writing · #389 of 402 · 45% 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), [Granite 4.0 350M](granite-4-0-350m.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty  counts  why
LMArena (text)                      1091.2  ▁▁●▂▃▃▃▆▆▇█▃ beats 14/385  avg 1260       24%  blunt
Polyrating rating (bias-aware re-…  1026.0  ▂▁▂▁●▇█▆▇▄▂▂ beats 17/95   avg 1187       22%  typical
other tests (3) 
test                                result  vs the others in its run   difficulty        counts  why
LiveBench 2024-07-26                 11.3%  ▁▁▁●▂▃▂█▆▃▅▃ beats 5/61    medium · avg 38%     45%  typical
Open PT-LLM Leaderboard average (…   61.3%  ▃▁▁▁▁▁▁▁●█▅▁ beats 93/276  medium · avg 72%    4.4%  at its level · noisy
EuroEval Life in the UK (English …   72.8%  ▁▁▁▁▁▁▁▂●█▄▃ beats 44/146  easy · avg 82%      3.8%  at its level · 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   45% ▇▇▇▇▇
LMArena leaderboard dataset                     1   24% ▇▇▇
Polyrating: A Cost-Effective and Bias-A…        1   22% ▇▇▇
Open Portuguese LLM Leaderboard                 1    4% ▇
EuroEval leaderboards                           1    4% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 34.2
Licences: LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · 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/)) · 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 4B Chat (Q4_K_M, needs ~2.5 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-4B-Chat-GGUF/tree/ff45e22352f7ff8d2be76ba26497fbf4ad0343e4)) ```sh llama cli -hf Qwen/Qwen1.5-4B-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/Qwen1.5-4B-Chat-GGUF:q4_k_m ``` Other versions: Q8_0 (~4.2 GB) `llama cli -hf Qwen/Qwen1.5-4B-Chat-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 Qwen1.5 4B Chat (Q4_K_M, needs ~2.5 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-4B-Chat-GGUF/tree/ff45e22352f7ff8d2be76ba26497fbf4ad0343e4)) ```sh lms get https://huggingface.co/Qwen/Qwen1.5-4B-Chat-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 (~4.2 GB) `lms get https://huggingface.co/Qwen/Qwen1.5-4B-Chat-GGUF@q8_0 -y` · 4bit (~2.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen1.5-4B-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 4B Chat (Q4_K_M, needs ~2.87 GB, from [ollama.com](https://ollama.com/library/qwen/tags)) ```sh ollama run qwen:4b-chat-v1.5-q4_K_M ``` Other versions: Q8_0 (~5.24 GB) `ollama run qwen:4b-chat-v1.5-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 Qwen1.5 4B Chat (4bit, needs ~2.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen1.5-4B-Chat-4bit/tree/a4662637532e01db96afcea3943c5001f0733550)) ```sh mlx_lm.chat --model mlx-community/Qwen1.5-4B-Chat-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/Qwen1.5-4B-Chat-4bit ``` Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-4B-Chat) [interactive version](https://whatmodel.sh/model/qwen1-5-4b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)