[whatmodel.sh](../../README.md) / CodeQwen1.5 7B Chat # CodeQwen1.5 7B Chat Alibaba · released 2024-04 · 7.25B params · tongyi-qianwen **score 51.6** ±0.7 · #340 of 732 · from 14 benchmark results across 3 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 Chat among every model on that task or test, worst to best. 
task    vs the others         rank  counts  tests
coding  ▁▁▁▃▅▇●▇▅▄▃▁   #248 of 469     82%      7
coding · #248 of 469 · 82% of its score Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [InternLM2.5 20B Chat](internlm2-5-20b-chat.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md) here, though they rank lower overall. 
test                result  vs the others in its run  difficulty        counts  why
HumanEval            83.5%  ▁·▁▁▃▄▆▅█●▁▁ beats 89/99  easy · avg 76%       34%  sharp · several runs
HumanEval+           78.7%  ▁·▁▁▁▃▄▄▄█●▁ beats 89/99  easy · avg 70%       31%  sharp · at its level · several runs
MBPP+                69.0%  ▁···▁▁▂▅▇█●▂ beats 61/75  easy · avg 66%      8.3%  at its level · several runs
MBPP (maker)         77.7%  █···██●·██·█ beats 3/6    easy · avg 77%      3.5%  typical
MultiPL-E (maker)    70.8%  ▄·▄····●▄·▄█ beats 2/6    easy · avg 70%      3.0%  at its level
CRUXEval-I (maker)   44.0%  █·●█·█··█·██ beats 1/6    medium · avg 56%    1.4%  at its level
CRUXEval-O (maker)   38.8%  ▄·●··▄·▄··▄▄ beats 2/6    medium · avg 54%    0.5%  blunt · at its level
other tests (3) 
test                                result  vs the others in its run  difficulty        counts  why
BigCodeBench (Instruct) (maker)      39.3%  ▃▂·▆▆██●▅▃▃▃ beats 24/33  medium · avg 39%    8.5%  at its level · several runs
BigCodeBench (Complete) (maker)      43.7%  ▂▁▁▄▂▅▇●▆▂▂▂ beats 39/58  medium · avg 48%    6.2%  at its level
EvalPerf correctness (118 tasks, …   74.1%  ▂·▂▂▂▆●▆▆▆▃█ beats 10/29  easy · avg 74%      3.0%  typical
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
EvalPlus / EvalPerf published evaluatio…        4   62% ▇▇▇▇▇▇▇
Qwen (submitted / maker-reported)               8   25% ▇▇▇
BigCodeBench paper (submitted / maker-r…        2   13% ▇▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 51.6
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 Chat (Q4_K_M, needs ~4.7 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat-GGUF/tree/1848b838202efec87a9afa95b6859e456be2ff25)) ```sh llama cli -hf Qwen/CodeQwen1.5-7B-Chat-GGUF:q4_k_m ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf Qwen/CodeQwen1.5-7B-Chat-GGUF:q4_k_m ``` Other versions: Q8_0 (~7.7 GB) `llama cli -hf Qwen/CodeQwen1.5-7B-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 CodeQwen1.5 7B Chat (Q4_K_M, needs ~4.7 GB, from [huggingface.co/Qwen](https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat-GGUF/tree/1848b838202efec87a9afa95b6859e456be2ff25)) ```sh lms get https://huggingface.co/Qwen/CodeQwen1.5-7B-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 (~7.7 GB) `lms get https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat-GGUF@q8_0 -y` · 6bit (~5.9 GB, Mac only) `lms get https://huggingface.co/Wwayu/CodeQwen1.5-7B-Chat-mlx-6Bit -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 Chat (Q4_K_M, needs ~4.85 GB, from [ollama.com](https://ollama.com/library/codeqwen/tags)) ```sh ollama run codeqwen:7b-chat-v1.5-q4_K_M ``` Other versions: Q8_0 (~9.2 GB) `ollama run codeqwen:7b-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 CodeQwen1.5 7B Chat (6bit, needs ~5.9 GB, Mac only, from [huggingface.co/Wwayu](https://huggingface.co/Wwayu/CodeQwen1.5-7B-Chat-mlx-6Bit/tree/73039b4e9d5e267f2af176864cb2fd1f01a0e0bd)) ```sh mlx_lm.chat --model Wwayu/CodeQwen1.5-7B-Chat-mlx-6Bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model Wwayu/CodeQwen1.5-7B-Chat-mlx-6Bit ``` Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat) [interactive version](https://whatmodel.sh/model/codeqwen1-5-7b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)