[whatmodel.sh](../../README.md) / Qwen2 57B-A14B Instruct
# Qwen2 57B-A14B Instruct
Alibaba · released 2024-06 · 57.41B params (14B active) · apache-2.0
**score 48.9** ±1.6 · #393 of 732 · from 5 benchmark results across 4 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Qwen2 57B-A14B Instruct among every model on that task or test, worst to best.
other tests (5)
test result vs the others in its run difficulty counts why
BigCodeBench (Instruct) (maker) 35.7% ▃▂·▆▆█●▆▅▃▃▃ beats 17/33 medium · avg 39% 32% at its level
BigCodeBench (Complete) (maker) 46.5% ▂▁▁▄▂▅▇█●▂▂▂ beats 44/58 medium · avg 48% 31% at its level
Polish EQ-Bench v2 (SpeakLeash) 57.6% ▁··▁▁▂▂▆▅●█▆ beats 36/70 medium · avg 62% 17% at its level
Open Persian LLM Leaderboard v2 a… 56.5% ▂▄▂▃█▄●▆▃▅▂▂ beats 46/81 medium · avg 57% 13% blunt · at its level
Open PT-LLM Leaderboard average (… 75.5% ▃▁▁▁▁▁▁▁▂█●▁ beats 229/276 medium · avg 72% 6.5% at its level · noisy
source results share of the score
BigCodeBench paper (submitted / maker-r… 2 63% ▇▇▇▇▇▇▇▇
SpeakLeash Polish EQ-Bench leaderboard 1 17% ▇▇
Open Persian LLM Leaderboard v2 1 13% ▇▇
Open Portuguese LLM Leaderboard 1 7% ▇
prior: pull toward the middle 1% ▇
weighted average = 48.9
Licences: BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Persian LLM Leaderboard v2 ([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 Qwen2 57B-A14B Instruct (Q4_K_M, needs ~34.9 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct-GGUF/tree/94f9b6bfa2811afec9ce190f14e4ab6a88687561))
```sh
llama cli -hf Qwen/Qwen2-57B-A14B-Instruct-GGUF:q4_k_m
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf Qwen/Qwen2-57B-A14B-Instruct-GGUF:q4_k_m
```
Other versions: Q8_0 (~61 GB) `llama cli -hf Qwen/Qwen2-57B-A14B-Instruct-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 Qwen2 57B-A14B Instruct (Q4_K_M, needs ~34.9 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct-GGUF/tree/94f9b6bfa2811afec9ce190f14e4ab6a88687561))
```sh
lms get https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct-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 (~61 GB) `lms get https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct-GGUF@q8_0 -y` · 4bit (~32.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2-57B-A14B-Instruct-4bit -y` · 8bit (~61 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2-57B-A14B-Instruct-8bit -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 Qwen2 57B-A14B Instruct (Q4_K_M, needs ~34.9 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct-GGUF/tree/94f9b6bfa2811afec9ce190f14e4ab6a88687561))
```sh
ollama run hf.co/Qwen/Qwen2-57B-A14B-Instruct-GGUF:q4_k_m
```
### 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 Qwen2 57B-A14B Instruct (4bit, needs ~32.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen2-57B-A14B-Instruct-4bit/tree/e9c653af4117bd4ee8f9302cc5cd060c9539cc30))
```sh
mlx_lm.chat --model mlx-community/Qwen2-57B-A14B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen2-57B-A14B-Instruct-4bit
```
Other versions: 8bit (~61 GB, Mac only) `mlx_lm.chat --model mlx-community/Qwen2-57B-A14B-Instruct-8bit`
### Other ways to run it
1. Run it as published (BF16, needs ~114.82 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct/tree/50896d66b39f1425d63720541a66c7df13e053c0))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='Qwen/Qwen2-57B-A14B-Instruct', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct)
[interactive version](https://whatmodel.sh/model/qwen2-57b-a14b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)