[whatmodel.sh](../../README.md) / Qwen-7B (base)
# Qwen-7B (base)
Alibaba · released 2023-08 · 7.72B params · other (tongyi-qianwen-license-agreement)
**score 32.8** ±1.0 · #644 of 732 · from 11 benchmark results across 2 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Qwen-7B (base) among every model on that task or test, worst to best.
other tests (11)
test result vs the others in its run difficulty counts why
ARC Challenge 49.1% ▁·▁··▁▁▇▂█▆● beats 24/26 easy · avg 82% 52% sharp · at its level
WinoGrande 66.0% ▁····▁··▁▂●▄ beats 14/26 easy · avg 86% 11% at its level
BoolQ 74.6% ▂▃▂▅▆▂▅▆●▂▂▂ beats 21/26 easy · avg 88% 7.9% at its level
HellaSwag 88.8% ▃···▁▇▅█▇▃▁● beats 26/26 very easy · avg 91% 5.2% at its level
ARC Easy 65.2% ▁▁·▁▁▄▅●█▅▁▃ beats 13/26 easy · avg 85% 5.1% blunt · at its level
OpenBookQA 32.2% █·▁▁▃·▁▃▅●▃▄ beats 21/26 hard · avg 49% 4.4% typical
PIQA 74.0% ▁·▁··▁▁●█▅█▂ beats 5/26 easy · avg 87% 3.8% blunt · at its level
SciQ 93.2% ▁·▁·▁·▃▅▃●▄▁ beats 15/26 very easy · avg 97% 3.8% blunt · maxed out
Social IQA 49.4% ▁···▁▃█▁▁●·▁ beats 25/26 hard · avg 56% 2.4% blunt
LAMBADA (OpenAI version), last-wo… 69.7% ▁··▂▁▁▁▂▆●▅▃ beats 13/26 very easy · avg 86% 2.2% blunt · at its level
Open PT-LLM Leaderboard average (… 58.1% ▃▁▁▁▁▁▁▁●█▅▁ beats 85/276 medium · avg 72% 1.8% noisy
source results share of the score
Stability AI 10 98% ▇▇▇▇▇▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard 1 2% ▇
prior: pull toward the middle 0% ▇
weighted average = 32.8
Licences: Stability AI ([Apache-2.0](../../licenses/stablelm-original-eval-json-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 Qwen-7B (base) (Q4_K_M, needs ~4.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Qwen-7B-GGUF/tree/40c967f3d68cd9a264b509550da30daf58199475))
```sh
llama cli -hf mradermacher/Qwen-7B-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-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.2 GB) `llama cli -hf mradermacher/Qwen-7B-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-7B (base) (Q4_K_M, needs ~4.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Qwen-7B-GGUF/tree/40c967f3d68cd9a264b509550da30daf58199475))
```sh
lms get https://huggingface.co/mradermacher/Qwen-7B-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 (~8.2 GB) `lms get https://huggingface.co/mradermacher/Qwen-7B-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-7B (base) (Q4_K_M, needs ~4.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Qwen-7B-GGUF/tree/40c967f3d68cd9a264b509550da30daf58199475))
```sh
ollama run hf.co/mradermacher/Qwen-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.2 GB) `ollama run hf.co/mradermacher/Qwen-7B-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (BF16, needs ~15.44 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen-7B/tree/ef3c5c9c57b252f3149c1408daf4d649ec8b6c85))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='Qwen/Qwen-7B', torch_dtype='auto', device_map='auto', trust_remote_code=True)('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen-7B)
[interactive version](https://whatmodel.sh/model/qwen-7b-base) · [JSON](https://whatmodel.sh/api?format=json&all=1)