Instructions to use CohereLabs/command-a-plus-05-2026-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CohereLabs/command-a-plus-05-2026-bf16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="CohereLabs/command-a-plus-05-2026-bf16")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("CohereLabs/command-a-plus-05-2026-bf16")
model = AutoModelForImageTextToText.from_pretrained("CohereLabs/command-a-plus-05-2026-bf16")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CohereLabs/command-a-plus-05-2026-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CohereLabs/command-a-plus-05-2026-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CohereLabs/command-a-plus-05-2026-bf16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/CohereLabs/command-a-plus-05-2026-bf16

SGLang

How to use CohereLabs/command-a-plus-05-2026-bf16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CohereLabs/command-a-plus-05-2026-bf16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CohereLabs/command-a-plus-05-2026-bf16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CohereLabs/command-a-plus-05-2026-bf16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CohereLabs/command-a-plus-05-2026-bf16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use CohereLabs/command-a-plus-05-2026-bf16 with Docker Model Runner:
```
docker model run hf.co/CohereLabs/command-a-plus-05-2026-bf16
```

alexrs commited on 1 day ago

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@@ -202,7 +202,8 @@ Command A+ has been specifically trained with conversational tool use capabiliti
 Tool use with Command A+ is supported through [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating#advanced-tool-use--function-calling) in Transformers. We recommend providing tool descriptions using JSON schema.
-**Tool Use Example**
 ```py
 from transformers import AutoTokenizer
@@ -266,8 +267,10 @@ conversation.append({"role": "tool", "tool_call_id": "0", "content": api_respons
 After that, you can generate() again to let the model use the tool result in the chat.
 Note that this was a very brief introduction to tool calling \- for more information, see the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).
-**Tool Use with citations**
 Optionally, one can ask the model to include grounding spans (citations) in its response to indicate the source of the information, by using `enable_citations=True` in `tokenizer.apply_chat_template(*)`. The generation would look like this:
@@ -276,6 +279,8 @@ On 29th September 2023, the total sales amount was <co>10000</co: 0:[0]> and the
 ```
 When citations are turned on, the model associates pieces of texts (called "spans") with those specific tool results that support them (called "sources"). Command A+ uses a pair of tags `<co>` and `</co>` to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. For example, `<co>span</co: 0:[1,2],1:[0]>` means that "span" is supported by result 1 and 2 from `tool_call_id=0` as well as result 0 from `tool_call_id=1`. Sources from the same tool call are grouped together and listed as `{tool_call_id}:[{list of result indices}]`, before they are joined together by ",".
 ## **Model Card Contact**

 Tool use with Command A+ is supported through [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating#advanced-tool-use--function-calling) in Transformers. We recommend providing tool descriptions using JSON schema.
+<details>
+<summary><b>Tool Use Example [CLICK TO EXPAND]</b></summary>
 ```py
 from transformers import AutoTokenizer
 After that, you can generate() again to let the model use the tool result in the chat.
 Note that this was a very brief introduction to tool calling \- for more information, see the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).
+</details>
+<details>
+<summary><b>Tool Use With Citations [CLICK TO EXPAND]</b></summary>
 Optionally, one can ask the model to include grounding spans (citations) in its response to indicate the source of the information, by using `enable_citations=True` in `tokenizer.apply_chat_template(*)`. The generation would look like this:
 ```
 When citations are turned on, the model associates pieces of texts (called "spans") with those specific tool results that support them (called "sources"). Command A+ uses a pair of tags `<co>` and `</co>` to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. For example, `<co>span</co: 0:[1,2],1:[0]>` means that "span" is supported by result 1 and 2 from `tool_call_id=0` as well as result 0 from `tool_call_id=1`. Sources from the same tool call are grouped together and listed as `{tool_call_id}:[{list of result indices}]`, before they are joined together by ",".
+</details>
 ## **Model Card Contact**