[whatmodel.sh](../../README.md) / OpenHermes-2.5-Code-290k-13B # OpenHermes-2.5-Code-290k-13B ajibawa-2023 · released 2024-03 · 13.02B params · apache-2.0 **score 42.1** ±0.8 · #532 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 OpenHermes-2.5-Code-290k-13B among every model on that task or test, worst to best.
task    vs the others         rank  counts  tests
coding  ▁▁▁▃●▇█▇▅▄▃▁   #370 of 469    100%      3
coding · #370 of 469 · 100% 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), [Code Tulu 2 13B](code-tulu-2-13b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md) here, though they rank lower overall.
test        result  vs the others in its run  difficulty      counts  why
HumanEval    54.3%  ▁·▁▁▃▄●▅█▃▁▁ beats 39/99  easy · avg 76%     52%  sharp · at its level
HumanEval+   48.8%  ▁·▁▁▁▃▄●▄█▃▁ beats 39/99  easy · avg 70%     40%  sharp · at its level
MBPP+        45.8%  ▁···▁▁▂●▇█▆▂ beats 11/75  easy · avg 66%    7.8%  at its level
## 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…        3  100% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 42.1
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 OpenHermes-2.5-Code-290k-13B (Q4_K_M, needs ~7.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/OpenHermes-2.5-Code-290k-13B-GGUF/tree/417e6f2dc47f531e1d9f769ef098855cdfa3ce03)) ```sh llama cli -hf mradermacher/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf mradermacher/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M ``` Other versions: Q8_0 (~13.8 GB) `llama cli -hf mradermacher/OpenHermes-2.5-Code-290k-13B-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 OpenHermes-2.5-Code-290k-13B (Q4_K_M, needs ~7.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/OpenHermes-2.5-Code-290k-13B-GGUF/tree/417e6f2dc47f531e1d9f769ef098855cdfa3ce03)) ```sh lms get https://huggingface.co/mradermacher/OpenHermes-2.5-Code-290k-13B-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 (~13.8 GB) `lms get https://huggingface.co/mradermacher/OpenHermes-2.5-Code-290k-13B-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 OpenHermes-2.5-Code-290k-13B (Q4_K_M, needs ~7.9 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/OpenHermes-2.5-Code-290k-13B-GGUF/tree/417e6f2dc47f531e1d9f769ef098855cdfa3ce03)) ```sh ollama run hf.co/mradermacher/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M ``` Other versions: Q8_0 (~13.8 GB) `ollama run hf.co/mradermacher/OpenHermes-2.5-Code-290k-13B-GGUF:Q8_0` ### Other ways to run it 1. Run it as published (BF16, needs ~26.03 GB, from [huggingface.co/ajibawa-2023](https://huggingface.co/ajibawa-2023/OpenHermes-2.5-Code-290k-13B/tree/8dcd6adc9b35c4f2912b4849ffa58f5be9a2daf5)) ```sh uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='ajibawa-2023/OpenHermes-2.5-Code-290k-13B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])" ``` Model repository and documentation: [huggingface.co/ajibawa-2023](https://huggingface.co/ajibawa-2023/OpenHermes-2.5-Code-290k-13B) [interactive version](https://whatmodel.sh/model/openhermes-2-5-code-290k-13b) · [JSON](https://whatmodel.sh/api?format=json&all=1)