[whatmodel.sh](../../README.md) / Qwen-14B-Chat
# Qwen-14B-Chat
Alibaba · released 2023-09 · 14.2B params · tongyi-qianwen
**score 40.2** ±0.9 · #562 of 732 · from 6 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 Qwen-14B-Chat among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #386 of 469* 39% 1
chat & writing ▁▁▁●▆▄▆█▆▇▇▃ #362 of 402 36% 3
coding · #386 of 469 · 39% of its score
Beats [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Tulu 2 70B](tulu-2-70b.md) here, though they rank higher overall. Trails [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [WizardLM 13B V1.2](wizardlm-13b-v1-2.md), [Falcon3 3B Instruct](falcon3-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 46.3% ▁█▄▅●█·▄▇▅▄▄ beats 18/42 easy · avg 75% 39% sharp · at its level
chat & writing · #362 of 402 · 36% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [Phi-3-mini-4k-instruct (June 2024 update)](phi-3-mini-4k-instruct-june-2024.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 H 350M](granite-4-0-h-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 12.4% ▁▂▂▃●▆█▆▄▄▁▁ beats 30/122 medium · avg 35% 20% sharp
LMArena (text) 1139.4 ▁▁▁●▃▃▃▆▆▇█▃ beats 32/385 avg 1260 8.7% blunt
Polyrating rating (bias-aware re-… 1081.0 ▂▁▂▁▃▇●▆▇▄▂▂ beats 37/95 avg 1187 8.0% typical
other tests (2)
test result vs the others in its run difficulty counts why
EvoEval (overall) 32.1% ▂▂▃▄▄●▁▄▅▃▁▃ beats 17/42 medium · avg 52% 21% at its level
EQ-Bench v1 (60 questions, paper … 43.8% ▂··▄·▂▆●▂█·▂ beats 8/14 medium · avg 53% 3.6% at its level
source results share of the score
Top Leaderboard Ranking = Top Coding Pr… 2 60% ▇▇▇▇▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards 1 20% ▇▇
LMArena leaderboard dataset 1 9% ▇
Polyrating: A Cost-Effective and Bias-A… 1 8% ▇
EQ-Bench: An Emotional Intelligence Ben… 1 4% ▇
prior: pull toward the middle 0% ▇
weighted average = 40.2
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)) · 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/)) · EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models ([CC BY 4.0](../../licenses/paper-2312.06281v2-CC-BY-4.0.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 Qwen-14B-Chat (Q4_K_M, needs ~9.4 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Qwen-14B-Chat-GGUF/tree/ec2501e3a5c01741788a74054113337bd75b02fd))
```sh
llama cli -hf mradermacher/Qwen-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 mradermacher/Qwen-14B-Chat-GGUF:Q4_K_M
```
Other versions: Q8_0 (~15.1 GB) `llama cli -hf mradermacher/Qwen-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 Qwen-14B-Chat (Q4_K_M, needs ~9.4 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Qwen-14B-Chat-GGUF/tree/ec2501e3a5c01741788a74054113337bd75b02fd))
```sh
lms get https://huggingface.co/mradermacher/Qwen-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/mradermacher/Qwen-14B-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 Qwen-14B-Chat (Q4_K_M, needs ~9.4 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Qwen-14B-Chat-GGUF/tree/ec2501e3a5c01741788a74054113337bd75b02fd))
```sh
ollama run hf.co/mradermacher/Qwen-14B-Chat-GGUF:Q4_K_M
```
Other versions: Q8_0 (~15.1 GB) `ollama run hf.co/mradermacher/Qwen-14B-Chat-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (BF16, needs ~28.33 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen-14B-Chat/blob/cdaff792392504e679496a9f386acf3c1e4333a5/README.md))
```sh
pip install torch transformers==4.32.0 accelerate tiktoken einops scipy transformers_stream_generator==0.0.4 && python -c "from transformers import AutoModelForCausalLM, AutoTokenizer; t=AutoTokenizer.from_pretrained('Qwen/Qwen-14B-Chat', trust_remote_code=True); m=AutoModelForCausalLM.from_pretrained('Qwen/Qwen-14B-Chat', device_map='auto', trust_remote_code=True).eval(); print(m.chat(t, '你好', history=None)[0])"
```
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen-14B-Chat)
[interactive version](https://whatmodel.sh/model/qwen-14b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)