[whatmodel.sh](../../README.md) / Athene-70B
# Athene-70B
Nexusflow · released 2024-07 · 70.6B params · nexusflow-research
**score 51.8** ±0.8 · #337 of 732 · from 8 benchmark results across 4 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Athene-70B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #307 of 469* 17% 1
chat & writing ▁▁▁▃▆▄▆●▆▇▇▃ #217 of 402* 23% 2
math ▁▁▃●█▆▄█▄▂▁▁ #352 of 431* 15% 2
coding · #307 of 469 · 17% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3-VL 30B-A3B Instruct](qwen3-vl-30b-a3b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Yi 1.5 9B Chat](yi-1-5-9b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CRUXEval (ZeroEval, 800 problems) 50.6% ▁▂▆●▆█▆▃▂·▁▁ beats 13/47 medium · avg 57% 17% sharp · at its level
chat & writing · #217 of 402 · 23% of its score
Beats [Gemma 2 9B IT SimPO](gemma-2-9b-simpo.md), [Granite 4.2 8B](granite-4-2-8b.md), [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [Granite 4.0 H Micro 3B](granite-4-0-h-micro.md), [Gemma 3n E4B](gemma-3n-e4b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena-Hard (v0.1, GPT-4-1106 judg… 77.6% ▂▁▁▅█▅▄▃▃●▃▁ beats 48/60 medium · avg 47% 17% sharp · at its level
LMArena (text) 1306.6 ▁▁▁▂▃▃▃●▆▇█▃ beats 136/385 avg 1260 6.7% blunt
math · #352 of 431 · 15% of its score
Trails [OPT-2.7B](opt-2-7b.md), [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH Level 5 (ZeroEval, 721 probl… 20.7% ▁▁▁▆●▂▇▇▄··▂ beats 9/38 hard · avg 30% 8.3% sharp · at its level
GSM8K (ZeroEval, zero-shot) 86.7% ▁▄·▁▄█▅●▅███ beats 18/43 very easy · avg 87% 6.7% sharp
other tests (3)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 76.6% ▁·▁▁▄▄▄●█▄▂▁ beats 23/53 easy · avg 75% 27% at its level
ZebraLogic (ZeroEval, 1,000 puzzl… 16.7% ▁▁▃▆●█▁▁·▁▁▁ beats 22/57 hard · avg 15% 10% sharp
WildBench v2 WB-Score (task macro) 59.5374 WB-Score ▁·▁▁·▃█▄▇▆▅● beats 52/52 avg 43.019 WB-Score 7.1% sharp
source results share of the score
WildEval ZeroEval published results 5 69% ▇▇▇▇▇▇▇▇
Arena-Hard-Auto v0.1 leaderboard 1 17% ▇▇
WildBench v2 leaderboard data 1 7% ▇
LMArena leaderboard dataset 1 7% ▇
prior: pull toward the middle 0% ▇
weighted average = 51.8
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## 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 Athene-70B (Q4_K_M, needs ~42.52 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Athene-70B-GGUF/tree/c7912e4ec93accee2e2b3f5d4bcb547ee2ed3bc8))
```sh
llama cli -hf bartowski/Athene-70B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/Athene-70B-GGUF:Q4_K_M
```
Other versions: Q2_K (~26.38 GB) `llama cli -hf bartowski/Athene-70B-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 Athene-70B (Q4_K_M, needs ~42.52 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Athene-70B-GGUF/tree/c7912e4ec93accee2e2b3f5d4bcb547ee2ed3bc8))
```sh
lms get https://huggingface.co/bartowski/Athene-70B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q2_K (~26.38 GB) `lms get https://huggingface.co/bartowski/Athene-70B-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 Athene-70B (Q4_K_M, needs ~42.52 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Athene-70B-GGUF/tree/c7912e4ec93accee2e2b3f5d4bcb547ee2ed3bc8))
```sh
ollama run hf.co/bartowski/Athene-70B-GGUF:Q4_K_M
```
Other versions: Q2_K (~26.38 GB) `ollama run hf.co/bartowski/Athene-70B-GGUF:Q2_K`
Model repository and documentation: [huggingface.co/Nexusflow](https://huggingface.co/Nexusflow/Athene-70B)
[interactive version](https://whatmodel.sh/model/athene-70b) · [JSON](https://whatmodel.sh/api?format=json&all=1)