[whatmodel.sh](../../README.md) / Qwen1.5 14B Chat
# Qwen1.5 14B Chat
Alibaba · released 2024-02 · 14.2B params · tongyi-qianwen
**score 42.8** ±0.8 · #520 of 732 · from 11 benchmark results across 9 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 14B Chat among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #373 of 469* 30% 1
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #320 of 402 35% 4
coding · #373 of 469 · 30% of its score
Beats [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md) here, though they rank higher overall. Trails [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [Code Tulu 2 13B](code-tulu-2-13b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 50.0% ▁█▄▅▇●·▄▇▅▄▄ beats 20/42 easy · avg 75% 30% sharp · at its level
chat & writing · #320 of 402 · 35% 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
AlpacaEval 2 length-controlled 23.9% ▁▂▂▃▆▆●▆▄▄▁▁ beats 62/122 medium · avg 35% 18% sharp
LMArena (text) 1191.1 ▁▁▁▂●▃▃▆▆▇█▃ beats 65/385 avg 1260 6.6% blunt
Polyrating rating (bias-aware re-… 1112.0 ▂▁▂▁▃▇█●▇▄▂▂ beats 54/95 avg 1187 6.1% typical
Arabic IFEval prompt-level (Incep… 5.0% ▁·▁▁●▁▄▄▆▄█▄ beats 3/38 medium · avg 46% 4.2% sharp · at its level
other tests (6)
test result vs the others in its run difficulty counts why
EvoEval (overall) 33.8% ▂▂▃▄▄●▁▄▅▃▁▃ beats 22/42 medium · avg 52% 17% at its level
MMLU-Pro 36.2% ▃▂●█▃·█▃▅··▂ beats 3/23 medium · avg 54% 10% at its level
Polish EQ-Bench v2 (SpeakLeash) 51.3% ▁··▁▁▂▂▆●▆█▆ beats 26/70 medium · avg 62% 4.1% at its level
Open PT-LLM Leaderboard average (… 72.6% ▃▁▁▁▁▁▁▁▂●▅▁ beats 198/276 medium · avg 72% 1.6% at its level · noisy
EuroEval HellaSwag (English), tes… 79.6% ▃·▁▁▂▁▂▅█●▄▁ beats 71/100 medium · avg 67% 1.2% sharp · at its level · noisy
EuroEval Life in the UK (English … 82.8% ▁▁▁▁▁▁▁▂▄●▄▃ beats 82/146 easy · avg 82% 1.0% at its level · noisy
source results share of the score
Top Leaderboard Ranking = Top Coding Pr… 2 47% ▇▇▇▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards 1 18% ▇▇
MMLU-Pro author-released evaluation runs 1 10% ▇
LMArena leaderboard dataset 1 7% ▇
Polyrating: A Cost-Effective and Bias-A… 1 6% ▇
Inception Arabic/Hindi leaderboards 1 4% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 4% ▇
EuroEval leaderboards 2 2% ▇
Open Portuguese LLM Leaderboard 1 2% ▇
prior: pull toward the middle 0% ▇
weighted average = 42.8
Licences: Top Leaderboard Ranking = Top Coding Proficiency ([CC BY 4.0](../../licenses/paper-2403.19114v1-CC-BY-4.0.txt)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · MMLU-Pro author-released evaluation runs ([Apache-2.0](../../licenses/mmlu-pro-author-runs-Apache-2.0.txt)) · 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/)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · 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 14B Chat (Q4_K_M, needs ~9.2 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-14B-Chat-GGUF/tree/1e2e136ec2ff4e5ea297d4da75581b6bd4b40ca8))
```sh
llama cli -hf Qwen/Qwen1.5-14B-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/Qwen1.5-14B-Chat-GGUF:q4_k_m
```
Other versions: Q8_0 (~15.1 GB) `llama cli -hf Qwen/Qwen1.5-14B-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 14B Chat (Q4_K_M, needs ~9.2 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-14B-Chat-GGUF/tree/1e2e136ec2ff4e5ea297d4da75581b6bd4b40ca8))
```sh
lms get https://huggingface.co/Qwen/Qwen1.5-14B-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 (~15.1 GB) `lms get https://huggingface.co/Qwen/Qwen1.5-14B-Chat-GGUF@q8_0 -y` · 4bit (~9.1 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen1.5-14B-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 14B Chat (Q4_K_M, needs ~9.02 GB, from [ollama.com](https://ollama.com/library/qwen/tags))
```sh
ollama run qwen:14b-chat-v1.5-q4_K_M
```
Other versions: Q8_0 (~17.54 GB) `ollama run qwen:14b-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 14B Chat (4bit, needs ~9.1 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen1.5-14B-Chat-4bit/tree/10cbaecfdf0164ecc66ef1e1ed33984ae5278796))
```sh
mlx_lm.chat --model mlx-community/Qwen1.5-14B-Chat-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen1.5-14B-Chat-4bit
```
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-14B-Chat)
[interactive version](https://whatmodel.sh/model/qwen1-5-14b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)