[whatmodel.sh](../../README.md) / CodeQwen1.5 7B (base)
# CodeQwen1.5 7B (base)
Alibaba · released 2024-04 · 7.25B params · other (tongyi-qianwen-research)
**score 42.4** ±0.8 · #528 of 732 · from 4 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 CodeQwen1.5 7B (base) among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #378 of 469 93% 3
coding · #378 of 469 · 93% 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), [WizardLM 13B V1.2](wizardlm-13b-v1-2.md), [Granite 3.0 2B Instruct](granite-3-0-2b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 51.8% ▁·▁▁▃▄●▅█▃▁▁ beats 37/99 easy · avg 76% 48% sharp · at its level
HumanEval+ 45.7% ▁·▁▁▁▃▄●▄█▃▁ beats 38/99 easy · avg 70% 38% sharp · at its level
MBPP+ 60.8% ▁···▁▁▂▅▇●▆▂ beats 38/75 easy · avg 66% 7.3% at its level
other tests (1)
test result vs the others in its run difficulty counts why
BigCodeBench (Complete) 45.6% ▂▁▁▄▂▅▇█●▂▂▂ beats 43/58 medium · avg 48% 6.5% at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 93% ▇▇▇▇▇▇▇▇▇▇▇
BigCodeBench paper 1 7% ▇
prior: pull toward the middle 0% ▇
weighted average = 42.4
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/))
## 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 CodeQwen1.5 7B (base) (Q4_K_M, needs ~4.7 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/CodeQwen1.5-7B-GGUF/tree/c0bd1493818461df5d79ee2cde2651fc237d49a5))
```sh
llama cli -hf bartowski/CodeQwen1.5-7B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/CodeQwen1.5-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `llama cli -hf bartowski/CodeQwen1.5-7B-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 CodeQwen1.5 7B (base) (Q4_K_M, needs ~4.7 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/CodeQwen1.5-7B-GGUF/tree/c0bd1493818461df5d79ee2cde2651fc237d49a5))
```sh
lms get https://huggingface.co/bartowski/CodeQwen1.5-7B-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 (~7.7 GB) `lms get https://huggingface.co/bartowski/CodeQwen1.5-7B-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 CodeQwen1.5 7B (base) (Q4_K_M, needs ~4.7 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/CodeQwen1.5-7B-GGUF/tree/c0bd1493818461df5d79ee2cde2651fc237d49a5))
```sh
ollama run hf.co/bartowski/CodeQwen1.5-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `ollama run hf.co/bartowski/CodeQwen1.5-7B-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (BF16, needs ~14.5 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/CodeQwen1.5-7B/tree/5ce5a1554e50a9e3bb236de7c0b8a2a1746186e4))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='Qwen/CodeQwen1.5-7B', 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/CodeQwen1.5-7B)
[interactive version](https://whatmodel.sh/model/codeqwen1-5-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)