[whatmodel.sh](../../README.md) / OpenCodeInterpreter-DS-6.7B # OpenCodeInterpreter-DS-6.7B M-A-P · released 2024-02 · 6.74B params · apache-2.0 **score 50.6** ±0.9 · #355 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-6.7B among every model on that task or test, worst to best.
task vs the others rank counts tests coding ▁▁▁▃▅▇●▇▅▄▃▁ #276 of 469 100% 3
test result vs the others in its run difficulty counts why HumanEval 77.4% ▁·▁▁▃▄▆▅●▃▁▁ beats 80/99 easy · avg 76% 47% sharp HumanEval+ 72.0% ▁·▁▁▁▃▄▄▄●▃▁ beats 78/99 easy · avg 70% 43% sharp · at its level MBPP+ 66.4% ▁···▁▁▂▅▇●▆▂ beats 54/75 easy · avg 66% 9.2% at its level
source results share of the score EvalPlus / EvalPerf published evaluatio… 3 100% ▇▇▇▇▇▇▇▇▇▇▇▇ prior: pull toward the middle 0% ▇ weighted average = 50.6Licences: 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-6.7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/MaziyarPanahi](https://huggingface.co/MaziyarPanahi/OpenCodeInterpreter-DS-6.7B-GGUF/tree/bd35d131dcb21400ac709e1e12a382264f6bfd95)) ```sh llama cli -hf MaziyarPanahi/OpenCodeInterpreter-DS-6.7B-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf MaziyarPanahi/OpenCodeInterpreter-DS-6.7B-GGUF:Q4_K_M ``` Other versions: Q8_0 (~7.2 GB) `llama cli -hf MaziyarPanahi/OpenCodeInterpreter-DS-6.7B-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 OpenCodeInterpreter-DS-6.7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/MaziyarPanahi](https://huggingface.co/MaziyarPanahi/OpenCodeInterpreter-DS-6.7B-GGUF/tree/bd35d131dcb21400ac709e1e12a382264f6bfd95)) ```sh lms get https://huggingface.co/MaziyarPanahi/OpenCodeInterpreter-DS-6.7B-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 (~7.2 GB) `lms get https://huggingface.co/MaziyarPanahi/OpenCodeInterpreter-DS-6.7B-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 OpenCodeInterpreter-DS-6.7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/MaziyarPanahi](https://huggingface.co/MaziyarPanahi/OpenCodeInterpreter-DS-6.7B-GGUF/tree/bd35d131dcb21400ac709e1e12a382264f6bfd95)) ```sh ollama run hf.co/MaziyarPanahi/OpenCodeInterpreter-DS-6.7B-GGUF:Q4_K_M ``` Other versions: Q8_0 (~7.2 GB) `ollama run hf.co/MaziyarPanahi/OpenCodeInterpreter-DS-6.7B-GGUF:Q8_0` ### Other ways to run it 1. Run it as published (BF16, needs ~13.48 GB, from [huggingface.co/m-a-p](https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-6.7B/tree/60b89884df814590abd76757a6db4a527cbdfc91)) ```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-6.7B', 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-6.7B) [interactive version](https://whatmodel.sh/model/opencodeinterpreter-ds-6-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)