[whatmodel.sh](../../README.md) / OpenCodeInterpreter-DS-33B
# OpenCodeInterpreter-DS-33B
M-A-P · released 2024-02 · apache-2.0
**score 51.4** ±0.9 · #342 of 732 · from 3 benchmark results across 1 source
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks OpenCodeInterpreter-DS-33B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #264 of 469 100% 3
coding · #264 of 469 · 100% 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 [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [QwQ-32B-Preview](qwq-32b-preview.md), [InternLM2.5 20B Chat](internlm2-5-20b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 79.3% ▁·▁▁▃▄▆▅●▃▁▁ beats 82/99 easy · avg 76% 47% sharp
HumanEval+ 73.8% ▁·▁▁▁▃▄▄▄●▃▁ beats 83/99 easy · avg 70% 43% sharp · at its level
MBPP+ 68.5% ▁···▁▁▂▅▇█●▂ beats 57/75 easy · avg 66% 9.4% at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 100% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 0% ▇
weighted average = 51.4
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-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 OpenCodeInterpreter-DS-33B (Q4_K_M, needs ~19.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/OpenCodeInterpreter-DS-33B-GGUF/tree/22e217e2df0346670c39d7de285d8f78d9a2304a))
```sh
llama cli -hf mradermacher/OpenCodeInterpreter-DS-33B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/OpenCodeInterpreter-DS-33B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~35.4 GB) `llama cli -hf mradermacher/OpenCodeInterpreter-DS-33B-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 OpenCodeInterpreter-DS-33B (Q4_K_M, needs ~19.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/OpenCodeInterpreter-DS-33B-GGUF/tree/22e217e2df0346670c39d7de285d8f78d9a2304a))
```sh
lms get https://huggingface.co/mradermacher/OpenCodeInterpreter-DS-33B-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 (~35.4 GB) `lms get https://huggingface.co/mradermacher/OpenCodeInterpreter-DS-33B-GGUF@Q8_0 -y` · 4bit (~19.1 GB, Mac only) `lms get https://huggingface.co/mlx-community/OpenCodeInterpreter-DS-33B-hf-4bit-mlx -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 OpenCodeInterpreter-DS-33B (Q4_K_M, needs ~19.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/OpenCodeInterpreter-DS-33B-GGUF/tree/22e217e2df0346670c39d7de285d8f78d9a2304a))
```sh
ollama run hf.co/mradermacher/OpenCodeInterpreter-DS-33B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~35.4 GB) `ollama run hf.co/mradermacher/OpenCodeInterpreter-DS-33B-GGUF: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 OpenCodeInterpreter-DS-33B (4bit, needs ~19.1 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/OpenCodeInterpreter-DS-33B-hf-4bit-mlx/tree/889f9a13b4be5dcdbed710ab26136a00d63dc6df))
```sh
mlx_lm.chat --model mlx-community/OpenCodeInterpreter-DS-33B-hf-4bit-mlx
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/OpenCodeInterpreter-DS-33B-hf-4bit-mlx
```
### Other ways to run it
1. Run it as published (BF16, needs ~66.69 GB, from [huggingface.co/m-a-p](https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-33B/tree/c7d9400bd7d099d22c971046ba7f455dab91ee8a))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='m-a-p/OpenCodeInterpreter-DS-33B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/m-a-p](https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-33B)
[interactive version](https://whatmodel.sh/model/opencodeinterpreter-ds-33b) · [JSON](https://whatmodel.sh/api?format=json&all=1)