[whatmodel.sh](../../README.md) / Zephyr ORPO 141B-A35B v0.1 # Zephyr ORPO 141B-A35B v0.1 Hugging Face · released 2024-04 · 141B params (39B active) · apache-2.0 **score 46.5** ±1.8 · #445 of 732 · from 4 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 Zephyr ORPO 141B-A35B v0.1 among every model on that task or test, worst to best. 
task            vs the others         rank  counts  tests
chat & writing  ▁▁▁▃▆●▆█▆▇▇▃   #303 of 402     75%      3
chat & writing · #303 of 402 · 75% of its score Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md) here, though they rank higher overall. Trails [LFM2 350M](lfm2-350m.md), [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [LFM2 700M](lfm2-700m.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty      counts  why
LMArena (text)                      1213.1  ▁▁▁▂▃●▃▆▆▇█▃ beats 75/385  avg 1260           36%  blunt
Polyrating rating (bias-aware re-…  1143.0  ▂▁▂▁▃▇█●▇▄▂▂ beats 63/95   avg 1187           33%  typical
IFEval (aggregation unspecified) …   65.1%  ▁▂▄▂●▄▆██▅▂▁ beats 27/145  easy · avg 70%    6.7%  at its level · small run
other tests (1) 
test                    result  vs the others in its run   difficulty        counts  why
BIG-Bench Hard (maker)   59.0%  ▁▁▁▁▂▄▅●█▃▃▁ beats 83/155  medium · avg 62%     23%  sharp · at its level · small run
## 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
LMArena leaderboard dataset                     1   36% ▇▇▇▇
Polyrating: A Cost-Effective and Bias-A…        1   33% ▇▇▇▇
HuggingFaceH4 zephyr-orpo-141b-A35b-v0.…        2   30% ▇▇▇▇
prior: pull toward the middle                        2% ▇
weighted average                                   = 46.5
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · HuggingFaceH4 zephyr-orpo-141b-A35b-v0.1 model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) ## 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 Zephyr ORPO 141B-A35B v0.1 (Q4_K_M, needs ~85.6 GB, from [huggingface.co/MaziyarPanahi](https://huggingface.co/MaziyarPanahi/zephyr-orpo-141b-A35b-v0.1-GGUF/tree/8db6013940a7ed35c589cf7f2cb70893e5a15641)) ```sh llama cli -hf MaziyarPanahi/zephyr-orpo-141b-A35b-v0.1-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf MaziyarPanahi/zephyr-orpo-141b-A35b-v0.1-GGUF:Q4_K_M ``` Other versions: Q8_0 (~149.4 GB) `llama cli -hf MaziyarPanahi/zephyr-orpo-141b-A35b-v0.1-GGUF:Q8_0` · Q2_K (~52.1 GB) `llama cli -hf MaziyarPanahi/zephyr-orpo-141b-A35b-v0.1-GGUF:Q2_K` ### 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 Zephyr ORPO 141B-A35B v0.1 (Q4_K_M, needs ~85.6 GB, from [huggingface.co/MaziyarPanahi](https://huggingface.co/MaziyarPanahi/zephyr-orpo-141b-A35b-v0.1-GGUF/tree/8db6013940a7ed35c589cf7f2cb70893e5a15641)) ```sh lms get https://huggingface.co/MaziyarPanahi/zephyr-orpo-141b-A35b-v0.1-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 (~149.4 GB) `lms get https://huggingface.co/MaziyarPanahi/zephyr-orpo-141b-A35b-v0.1-GGUF@Q8_0 -y` · Q2_K (~52.1 GB) `lms get https://huggingface.co/MaziyarPanahi/zephyr-orpo-141b-A35b-v0.1-GGUF@Q2_K -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 Zephyr ORPO 141B-A35B v0.1 (Q4_0, needs ~85.1 GB, from [ollama.com](https://ollama.com/library/zephyr/tags)) ```sh ollama run zephyr:141b-v0.1-q4_0 ``` Other versions: Q8_0 (~169.7 GB) `ollama run zephyr:141b-v0.1-q8_0` Model repository and documentation: [huggingface.co/HuggingFaceH4](https://huggingface.co/HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1) [interactive version](https://whatmodel.sh/model/zephyr-orpo-141b-a35b) · [JSON](https://whatmodel.sh/api?format=json&all=1)