[whatmodel.sh](../../README.md) / Qwen1.5 32B Chat # Qwen1.5 32B Chat Alibaba · released 2024-04 · 32.51B params · tongyi-qianwen **score 45.6** ±1.3 · #460 of 732 · from 7 benchmark results across 6 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 32B Chat among every model on that task or test, worst to best.}

task            vs the others          rank  counts  tests
chat & writing  ▁▁▁▃▆●▆█▆▇▇▃   #310 of 402*     34%      2

chat & writing · #310 of 402 · 34% 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
LMArena (text)                      1204.1  ▁▁▁▂▃●▃▆▆▇█▃ beats 70/385  avg 1260       18%  blunt
Polyrating rating (bias-aware re-…  1126.0  ▂▁▂▁▃▇█●▇▄▂▂ beats 60/95   avg 1187       16%  typical

other tests (5)

test                                result  vs the others in its run    difficulty        counts  why
BigCodeBench (Complete) (maker)      41.0%  ▂▁▁▄▂▅●█▆▂▂▂ beats 30/58    medium · avg 48%     19%  at its level
BigCodeBench (Instruct) (maker)      31.8%  ▃▂·▆●██▆▅▃▃▃ beats 9/33     medium · avg 39%     19%  at its level
GPQA Diamond                         30.7%  ▁▁▁▁●▄▆▄▅▇█▅ beats 19/211   hard · avg 39%       11%  sharp
Polish EQ-Bench v2 (SpeakLeash)      60.7%  ▁··▁▁▂▂▆▅●█▆ beats 41/70    medium · avg 62%     11%  at its level
Open PT-LLM Leaderboard average (…   74.8%  ▃▁▁▁▁▁▁▁▂█●▁ beats 223/276  medium · avg 72%    4.3%  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
BigCodeBench paper (submitted / maker-r…        2   39% ▇▇▇▇▇
LMArena leaderboard dataset                     1   18% ▇▇
Polyrating: A Cost-Effective and Bias-A…        1   16% ▇▇
Epoch AI                                        1   11% ▇
SpeakLeash Polish EQ-Bench leaderboard          1   11% ▇
Open Portuguese LLM Leaderboard                 1    4% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 45.6

_{Licences: BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · 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/)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · 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 Qwen1.5 32B Chat (Q4_K_M, needs ~19.7 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-32B-Chat-GGUF/tree/6f7c0664ab4a89b5d5c7bf14b36b66b2ceaf41d4)) ```sh llama cli -hf Qwen/Qwen1.5-32B-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/Qwen1.5-32B-Chat-GGUF:q4_k_m ``` Other versions: Q8_0 (~34.6 GB) `llama cli -hf Qwen/Qwen1.5-32B-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 32B Chat (Q4_K_M, needs ~19.7 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-32B-Chat-GGUF/tree/6f7c0664ab4a89b5d5c7bf14b36b66b2ceaf41d4)) ```sh lms get https://huggingface.co/Qwen/Qwen1.5-32B-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 (~34.6 GB) `lms get https://huggingface.co/Qwen/Qwen1.5-32B-Chat-GGUF@q8_0 -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 32B Chat (Q4_0, needs ~20.01 GB, from [ollama.com](https://ollama.com/library/qwen/tags)) ```sh ollama run qwen:32b-chat ``` Other versions: Q8_0 (~39.51 GB) `ollama run qwen:32b-chat-v1.5-q8_0` Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-32B-Chat) [interactive version](https://whatmodel.sh/model/qwen1-5-32b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)