Parameters
35B
Context Length
128K
Modality
Text
Architecture
Dense
License
CC-BY-NC
Release Date
11 Mar 2024
Knowledge Cutoff
Jun 2024
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
4x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
128,000 tokens
Consumer
5x RTX 4090
24GB VRAM
Datacenter
2x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Rank
#141
| Benchmark | Score | Rank |
|---|---|---|
General Text | 1226 | 155 |
Overall Rank
#141
Coding Rank
-
Cohere Command R is a generative language model architected specifically for high-performance enterprise workloads, with an emphasis on long-context processing and tool-augmented workflows. Built on an optimized decoder-only Transformer framework, the model utilizes Grouped Query Attention (GQA) to maintain a significant 128,000-token context window while reducing the memory overhead typically associated with large-scale attention mechanisms. It is designed to facilitate the transition from experimental prototypes to production-grade deployments by offering a balance between inference efficiency and high-fidelity output.
The model undergoes a multi-stage training process including extensive pre-training on a diverse multilingual corpus and subsequent alignment via supervised fine-tuning and preference optimization. A defining architectural feature is its native training for grounded generation, which allows the model to produce responses with precise inline citations from external document sources. This makes it particularly effective for retrieval-augmented generation (RAG) pipelines, where maintaining factual consistency and source traceability is a primary requirement. Furthermore, Command R supports sophisticated multi-step tool use, enabling it to act as an agent that can reason through complex tasks by interacting with external APIs, databases, and software tools.
Optimized for global business applications, Command R provides native support for 10 languages and is trained on 23 in total, ensuring versatility across international markets. The architecture incorporates advanced components such as Rotary Positional Embeddings (RoPE) and Layer Normalization to ensure stability and coherence when handling massive input sequences. By focusing on practical utility in tasks like document summarization, complex reasoning, and structured data analysis, Command R serves as a scalable backbone for automated enterprise systems and intelligent agentic workflows.
Attention
Attention Structure
Multi-Head Attention
Attention Heads
64
Key-Value Heads
8
Attention Head Dimension
-
Position Embedding
Absolute Position Embedding
RoPE Theta
-
Sliding Window Attention
-
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
Layer Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
8,192
Number of Layers
40
FFN Intermediate Size (Dense)
-
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
-
APX AI
Online