[whatmodel.sh](../../README.md) / Command R (03-2024)
# Command R (03-2024)
Cohere · released 2024-03 · 35B params · cc-by-nc-4.0
**score 43.5** ±0.7 · #508 of 732 · from 15 benchmark results across 12 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Command R (03-2024) among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #283 of 402 19% 3
math ▁▁●▅█▆▄█▄▂▁▁ #415 of 431* 11% 1
chat & writing · #283 of 402 · 19% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Ministral 3 14B](ministral-3-14b.md), [Ministral 3 8B](ministral-3-8b.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [Flan-T5 XXL](flan-t5-xxl.md), [LFM2 1.2B](lfm2-1-2b.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… 17.0% ▂▁▁●█▅▄▃▃▅▃▁ beats 10/60 medium · avg 47% 8.9% sharp
LMArena (text) 1261.8 ▁▁▁▂▃▃●▆▆▇█▃ beats 97/385 avg 1260 5.3% blunt
Polyrating rating (bias-aware re-… 1175.0 ▂▁▂▁▃▇█▆●▄▂▂ beats 75/95 avg 1187 4.9% typical
math · #415 of 431 · 11% of its score
Trails [GPT-Neo 125M](gpt-neo-125m.md), [OPT-125M](opt-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (ZeroEval, zero-shot) 53.0% ▁●·▁▄█▅▅▅███ beats 2/43 very easy · avg 87% 11% sharp · at its level
other tests (11)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 61.1% ▁·▁▁●▄▄▅█▄▂▁ beats 3/53 easy · avg 75% 26% at its level
LiveBench 2024-07-26 26.8% ▁▁▁▂▂▃●█▆▃▅▃ beats 20/61 medium · avg 38% 17% at its level
MMLU-Pro 36.6% ▃▂●█▃·█▃▅··▂ beats 5/23 medium · avg 54% 8.0% at its level
WildBench v2 WB-Score (task macro) 29.5331 WB-Score ▁·▁▁·▃●▄▇▆▅▆ beats 12/52 avg 43.019 WB-Score 5.6% sharp
Polish EQ-Bench v2 (SpeakLeash) 56.4% ▁··▁▁▂▂▆▅●█▆ beats 33/70 medium · avg 62% 3.3% at its level
ZebraLogic (ZeroEval, 1,000 puzzl… 9.9% ▁▁▃●██▁▁·▁▁▁ beats 9/57 hard · avg 15% 3.0% sharp · too hard for it
Open Persian LLM Leaderboard v2 a… 55.4% ▂▄▂▃█▄●▆▃▅▂▂ beats 44/81 medium · avg 57% 2.5% blunt · at its level
RULER 64k 84.9% ▁▁·▁·▂▃▄●▂·▁ beats 15/22 easy · avg 82% 1.4% at its level
Open PT-LLM Leaderboard average (… 68.3% ▃▁▁▁▁▁▁▁▂●▅▁ beats 151/276 medium · avg 72% 1.3% at its level · noisy
EuroEval Life in the UK (English … 84.3% ▁▁▁▁▁▁▁▂▄●▄▃ beats 93/146 easy · avg 82% 0.8% at its level · noisy
RULER 128k 76.0% ▄··▂▄▂▆█●▄▄▂ beats 14/21 easy · avg 67% 0.4% at its level · noisy
source results share of the score
WildEval ZeroEval published results 3 40% ▇▇▇▇▇
LiveBench model judgments 1 17% ▇▇
Arena-Hard-Auto v0.1 leaderboard 1 9% ▇
MMLU-Pro author-released evaluation runs 1 8% ▇
WildBench v2 leaderboard data 1 6% ▇
LMArena leaderboard dataset 1 5% ▇
Polyrating: A Cost-Effective and Bias-A… 1 5% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 3% ▇
Open Persian LLM Leaderboard v2 1 3% ▇
NVIDIA RULER repository README results … 2 2% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
EuroEval leaderboards 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 43.5
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · MMLU-Pro author-released evaluation runs ([Apache-2.0](../../licenses/mmlu-pro-author-runs-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/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · NVIDIA RULER repository README results table ([Apache-2.0](https://github.com/NVIDIA/RULER/blob/c3f5e3b4f87f97e048793bb510a3a6b19a46bf3a/LICENSE)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE))
## 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 Command R (03-2024) (Q4_K_M, needs ~21.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/c4ai-command-r-v01-GGUF/tree/1d123d7a7cb5e6376d5a86e7bb4d4518561ffbfd))
```sh
llama cli -hf lmstudio-community/c4ai-command-r-v01-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/c4ai-command-r-v01-GGUF:Q4_K_M
```
Other versions: Q8_0 (~37.2 GB) `llama cli -hf lmstudio-community/c4ai-command-r-v01-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 Command R (03-2024) (Q4_K_M, needs ~21.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/c4ai-command-r-v01-GGUF/tree/1d123d7a7cb5e6376d5a86e7bb4d4518561ffbfd))
```sh
lms get https://huggingface.co/lmstudio-community/c4ai-command-r-v01-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 (~37.2 GB) `lms get https://huggingface.co/lmstudio-community/c4ai-command-r-v01-GGUF@Q8_0 -y` · 4bit (~22.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/c4ai-command-r-v01-4bit -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 Command R (03-2024) (Q4_K_M, needs ~21.5 GB, from [ollama.com](https://ollama.com/library/command-r/tags))
```sh
ollama run command-r:35b-v0.1-q4_K_M
```
Other versions: Q8_0 (~42.5 GB) `ollama run command-r:35b-v0.1-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Command R (03-2024) (4bit, needs ~22.7 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/c4ai-command-r-v01-4bit/tree/ffa5be21a2cb3fbcd0f3cedc0a60ada8fae98c73))
```sh
mlx_lm.chat --model mlx-community/c4ai-command-r-v01-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/c4ai-command-r-v01-4bit
```
Model repository and documentation: [huggingface.co/CohereLabs](https://huggingface.co/CohereLabs/c4ai-command-r-v01)
[interactive version](https://whatmodel.sh/model/command-r-03-2024) · [JSON](https://whatmodel.sh/api?format=json&all=1)