[whatmodel.sh](../../README.md) / Qwen1.5 110B Chat
# Qwen1.5 110B Chat
Alibaba · released 2024-04 · 111B params · tongyi-qianwen
**score 48.4** ±0.8 · #402 of 732 · from 8 benchmark results across 7 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 110B Chat among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #342 of 469* 14% 1
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #286 of 402 40% 3
coding · #342 of 469 · 14% of its score
Beats [Qwen3-VL 4B Instruct](qwen3-vl-4b-instruct.md), [LFM2.5 2.6B](lfm2-5-2-6b.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md) here, though they rank higher overall. Trails [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Aider code editing (133 Exercism … 37.6% ▁·▁▂●▁▆▄█▆▂▂ beats 6/62 medium · avg 51% 14% sharp · at its level
chat & writing · #286 of 402 · 40% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Ministral 3 8B](ministral-3-8b.md), [Gemma 4 E4B](gemma-4-e4b.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [LFM2 700M](lfm2-700m.md), [Flan-T5 XXL](flan-t5-xxl.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 43.9% ▁▂▂▃▆▆█▆●▄▁▁ beats 100/122 medium · avg 35% 27% sharp · at its level
LMArena (text) 1234.2 ▁▁▁▂▃●▃▆▆▇█▃ beats 85/385 avg 1260 7.1% blunt
Polyrating rating (bias-aware re-… 1161.0 ▂▁▂▁▃▇█▆●▄▂▂ beats 71/95 avg 1187 6.5% typical
other tests (4)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 29.8% ▁▁▁▂▂▃▂●▆▃▅▃ beats 27/61 medium · avg 38% 28% at its level
BigCodeBench (Instruct) (maker) 33.6% ▃▂·▆▆●█▆▅▃▃▃ beats 13/33 medium · avg 39% 8.2% at its level
BigCodeBench (Complete) (maker) 43.2% ▂▁▁▄▂▅▇●▆▂▂▂ beats 34/58 medium · avg 48% 8.0% at its level
Open PT-LLM Leaderboard average (… 74.7% ▃▁▁▁▁▁▁▁▂█●▁ beats 220/276 medium · avg 72% 1.7% at its level · noisy
source results share of the score
LiveBench model judgments 1 28% ▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards 1 27% ▇▇▇
BigCodeBench paper (submitted / maker-r… 2 16% ▇▇
Aider code editing benchmark runs 1 14% ▇▇
LMArena leaderboard dataset 1 7% ▇
Polyrating: A Cost-Effective and Bias-A… 1 7% ▇
Open Portuguese LLM Leaderboard 1 2% ▇
prior: pull toward the middle 0% ▇
weighted average = 48.4
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)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-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/)) · 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 110B Chat (Q4_K_M, needs ~67.2 GB, from [huggingface.co/second-state](https://huggingface.co/second-state/Qwen1.5-110B-Chat-GGUF/tree/6b1cec5e0989862ee45ddc5916e1357ec3d06097))
```sh
llama cli -hf second-state/Qwen1.5-110B-Chat-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf second-state/Qwen1.5-110B-Chat-GGUF:Q4_K_M
```
Other versions: Q8_0 (~118.2 GB) `llama cli -hf second-state/Qwen1.5-110B-Chat-GGUF:Q8_0` · Q2_K (~41.2 GB) `llama cli -hf second-state/Qwen1.5-110B-Chat-GGUF:Q2_K`
### 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 110B Chat (Q4_K_M, needs ~67.2 GB, from [huggingface.co/second-state](https://huggingface.co/second-state/Qwen1.5-110B-Chat-GGUF/tree/6b1cec5e0989862ee45ddc5916e1357ec3d06097))
```sh
lms get https://huggingface.co/second-state/Qwen1.5-110B-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 (~118.2 GB) `lms get https://huggingface.co/second-state/Qwen1.5-110B-Chat-GGUF@Q8_0 -y` · Q2_K (~41.2 GB) `lms get https://huggingface.co/second-state/Qwen1.5-110B-Chat-GGUF@Q2_K -y` · 4bit (~62.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen1.5-110B-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 110B Chat (Q4_K_M, needs ~67.1 GB, from [ollama.com](https://ollama.com/library/qwen/tags))
```sh
ollama run qwen:110b-chat-v1.5-q4_K_M
```
Other versions: Q8_0 (~133.7 GB) `ollama run qwen:110b-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 110B Chat (4bit, needs ~62.6 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen1.5-110B-Chat-4bit/tree/3d5fb9b2e76488b117578291dce066220251205f))
```sh
mlx_lm.chat --model mlx-community/Qwen1.5-110B-Chat-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen1.5-110B-Chat-4bit
```
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen1.5-110B-Chat)
[interactive version](https://whatmodel.sh/model/qwen1-5-110b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)