[whatmodel.sh](../../README.md) / Zephyr 7B alpha # Zephyr 7B alpha Hugging Face · released 2023-10 · 7.24B params · mit **score 38.3** ±1.1 · #592 of 732 · from 10 benchmark results across 6 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Zephyr 7B alpha among every model on that task or test, worst to best. 
task            vs the others         rank  counts  tests
chat & writing  ▁▁▁●▆▄▆█▆▇▇▃   #374 of 402     51%      4
chat & writing · #374 of 402 · 51% of its score Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [Phi-2 (2.7B)](phi-2.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 350M](granite-4-0-350m.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty           counts  why
AlpacaEval 2 length-controlled       10.3%  ▁▂▂▃●▆█▆▄▄▁▁ beats 22/122  medium · avg 35%        24%  sharp
LMArena (text)                      1127.0  ▁▁▁●▃▃▃▆▆▇█▃ beats 24/385  avg 1260                13%  blunt
Polyrating rating (bias-aware re-…  1051.0  ▂▁▂▁▃●█▆▇▄▂▂ beats 26/95   avg 1187                11%  typical
AlpacaEval 1.0 win rate (vs text-…   85.8%  ▂▂·▆·▇▃●█▆▄▂ beats 23/45   very easy · avg 94%    2.5%  sharp · noisy
other tests (6) 
test                                result  vs the others in its run    difficulty        counts  why
LiveBench 2024-07-26                 18.6%  ▁▁▁▂▂●▂█▆▃▅▃ beats 13/61    medium · avg 38%     31%  typical
Natural Questions (closed book)      26.2%  ▁·▁·▁▂▁▂·▅▂● beats 21/28    medium · avg 48%     12%  at its level
Open PT-LLM Leaderboard average (…   66.2%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 122/276  medium · avg 72%    2.7%  at its level · noisy
TriviaQA                             65.9%  ▁··▁█▃▃▁▄▅▄● beats 25/28    easy · avg 73%      1.5%  at its level · noisy
HaluEval QA hallucination detecti…   62.4%  █▁▁·▁▁·▁▁▁●▁ beats 25/27    hard · avg 66%      1.0%  noisy
TruthfulQA MC2                       56.0%  ▅▃█▆▁▂●···▁▁ beats 29/31    medium · avg 57%    0.3%  blunt · 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
LiveBench model judgments                       1   31% ▇▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards             2   27% ▇▇▇
Hallucinations Leaderboard results              4   15% ▇▇
LMArena leaderboard dataset                     1   13% ▇▇
Polyrating: A Cost-Effective and Bias-A…        1   11% ▇
Open Portuguese LLM Leaderboard                 1    3% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 38.3
Licences: LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Hallucinations Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · 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/)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-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 Zephyr 7B alpha (Q4_K_M, needs ~4.4 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/tree/834b8b6b1df954a81023f21d3c0ea2a8891480de)) ```sh llama cli -hf mradermacher/zephyr-7b-alpha-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf mradermacher/zephyr-7b-alpha-GGUF:Q4_K_M ``` Other versions: Q8_0 (~7.7 GB) `llama cli -hf mradermacher/zephyr-7b-alpha-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 Zephyr 7B alpha (Q4_K_M, needs ~4.4 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/zephyr-7b-alpha-GGUF/tree/834b8b6b1df954a81023f21d3c0ea2a8891480de)) ```sh lms get https://huggingface.co/mradermacher/zephyr-7b-alpha-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/mradermacher/zephyr-7b-alpha-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 Zephyr 7B alpha (Q4_K_M, needs ~4.84 GB, from [ollama.com](https://ollama.com/library/zephyr/tags)) ```sh ollama run zephyr:7b-alpha-q4_K_M ``` Other versions: Q8_0 (~9.19 GB) `ollama run zephyr:7b-alpha-q8_0` Model repository and documentation: [huggingface.co/HuggingFaceH4](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) [interactive version](https://whatmodel.sh/model/zephyr-7b-alpha) · [JSON](https://whatmodel.sh/api?format=json&all=1)