> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server.

# Command-R

[Cohere Command-R](https://cohere.com/command) is a series of enterprise-grade language models optimized for retrieval-augmented generation (RAG) and tool use. Command-R7B uses the updated `Cohere2ForCausalLM` architecture.

|                  |                                                   |
| ---------------- | ------------------------------------------------- |
| **Task**         | Text Generation                                   |
| **Architecture** | `CohereForCausalLM` / `Cohere2ForCausalLM`        |
| **Parameters**   | 7B – 104B                                         |
| **HF Org**       | [CohereForAI](https://huggingface.co/CohereForAI) |

## Available Models

* **c4ai-command-r-v01**: 35B
* **c4ai-command-r-plus**: 104B
* **c4ai-command-r7b-12-2024**: 7B (`Cohere2ForCausalLM`)

## Architectures

* `CohereForCausalLM` — Command-R v01, Plus
* `Cohere2ForCausalLM` — Command-R7B

## Example HF Models

| Model         | HF ID                                                                                                 |
| ------------- | ----------------------------------------------------------------------------------------------------- |
| Command-R v01 | [`CohereForAI/c4ai-command-r-v01`](https://huggingface.co/CohereForAI/c4ai-command-r-v01)             |
| Command-R7B   | [`CohereForAI/c4ai-command-r7b-12-2024`](https://huggingface.co/CohereForAI/c4ai-command-r7b-12-2024) |

## Example Recipes

| Recipe                                                                                                                                                          | Description                  |
| --------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------- |
| [cohere\_command\_r\_7b\_squad.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/cohere/cohere_command_r_7b_squad.yaml)            | SFT — Command-R 7B on SQuAD  |
| [cohere\_command\_r\_7b\_squad\_peft.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/cohere/cohere_command_r_7b_squad_peft.yaml) | LoRA — Command-R 7B on SQuAD |

## Try with NeMo AutoModel

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get the example recipes:

```bash
git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel
```

**3. Run the recipe** from inside the repo:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/cohere/cohere_command_r_7b_squad.yaml
```

**1. Pull the container** and mount a checkpoint directory:

```bash
docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.06.00
```

**2.** Navigate to the AutoModel directory (where the recipes are):

```bash
cd /opt/Automodel
```

**3. Run the recipe**:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/cohere/cohere_command_r_7b_squad.yaml
```

See the [Installation Guide](/get-started/installation) and [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft).

## Fine-Tuning

See the [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft).

## Hugging Face Model Cards

* [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
* [CohereForAI/c4ai-command-r7b-12-2024](https://huggingface.co/CohereForAI/c4ai-command-r7b-12-2024)