CohereLabs / command-a-plus-05-2026-bf16 like 139 Follow Cohere Labs 3.17k
Model Card for Command A+
Model Summary
Command A+ is an open source model with 25 billion active parameters and 218B total parameters model optimized for agentic, multilingual, and reasoning-heavy tasks with a focus on enterprise performance, while also providing support for vision inputs for processing image inputs.
Developed by: Cohere and Cohere Labs
- Point of Contact: Cohere Labs
- License: Apache 2.0
- Model: command-a-plus-05-2026
- Model Size: 25B active parameters, 218B total parameters
- Context length: 128K input
For more details about this model, please check out our blog post.
You can try out Command A+ before downloading the weights in our hosted Hugging Face Space.
Available quantizations
The following quantizations are available with example minimum GPU requirements
| Quantization | Blackwell | Hopper | | ------------------------------------------------------------------------------ | --------- | -------- | | BF16 (16-bit) | 4 x B200 | 8 x H100 | | FP8 (8-bit) | 2 x B200 | 4 x H100 | | W4A4 (4-bit) | 1 x B200 | 2 x H100 |
All three quantizations show negligible differences in benchmark quality and performance. Our recommended quantization for most uses is W4A4 which boasts superior speed and latency characteristics alongside a smaller hardware footprint.
For more details, please check out our blog post.
Usage
Transformers
Please install transformers from the source repository that includes the necessary changes for this model.
# pip install transformers
from transformers import AutoTokenizer, AutoModelForImageTextToTextmodel_id = "CohereLabs/command-a-plus-05-2026-bf16"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id)
Format message with the command-a-plus-05-2026-bf16 chat template
messages = [{"role": "user", "content": "What has keys but can't open locks?"}]
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
)gen_tokens = model.generate(
input_ids,
max_new_tokens=4096,
do_sample=True,
temperature=0.6,
top_p=0.95
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
As a result, you should get an output that looks like this, where the thinking is generated between the `<START_THINKING>` and `<END_THINKING>`:
<|START_THINKING|>The user asks a riddle: "What has keys but can't open locks?" The answer is a piano (or keyboard). So respond with answer.<|END_THINKING|>You can also use the model directly using transformers pipeline abstraction:
from transformers import pipeline
import torchmodel_id = "CohereLabs/command-a-plus-05-2026-bf16"
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline(
"text-generation",
model=model_id,
dtype="auto",
device_map="auto",
)
messages = [
{"role": "user", "content": "Explain the Transformer architecture"},
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
outputs = pipe(
messages,
max_new_tokens=300,
)
print(outputs[0]["generated_text"][-1])
vLLM
You can also run the model in vLLM. `vllm>=0.21.0` is required for Command A+ and accurate response parsing also requires installing Cohere’s melody library.
uv pip install vllm>=0.21.0
uv pip install transformers uv pip install cohere_melody>=0.9.0Then the vllm server can be started with the following command:
# This is for B200, adjust tp for your device vllm serve CohereLabs/command-a-plus-05-2026-bf16 -tp 4 --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choiceModel Details
Input: Text and images.
Output: Model generates text.
Model Architecture: Command A+ is a decoder-only Sparse Mixture-of-Experts Transformer Model. With 25B active parameters and 218B total parameters, it has 128 experts, out of which 8 are active per token, and a single shared expert is applied to all tokens. The attention layers interleave sliding-window attention layers with Rotational Positional Embeddings and global attention layers without positional embeddings in a 3:1 ratio, as first introduced in Command A. The sparse MoE layer is trained in a fully dropless manner and uses a token-choice router. We use additive-bias-based load balancing to encourage balanced token load across all experts, and swap out the softmax router activation function with a normalized sigmoid over the topk expert logits per token.
Languages covered: The model has been trained on 48 languages: English, Arabic, Bulgarian, Bengali, Catalan, Czech, Danish, German, Greek, Spanish, Estonian, Persian, Finnish, Filipino, French, Irish, Hebrew, Hindi, Croatian, Hungarian, Indonesian, Icelandic, Italian, Japanese, Korean, Lithuanian, Latvian, Malay, Maltese, Dutch, Norwegian, Punjabi, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Serbian, Swedish, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Vietnamese, Chinese.
Context Length: Command A+ supports a context length of 128K & 64K output length.
Tool Use Capabilities:
Command A+ has been specifically trained with conversational tool use capabilities. This allows the model to interact with external tools like APIs, databases, or search engines.
Tool use with Command A+ is supported through chat templates in Transformers. We recommend providing tool descriptions using JSON schema.
Tool Use Example \[CLICK TO EXPAND\]
from transformers import AutoTokenizermodel_id = "CohereLabs/command-a-plus-05-2026-bf16"
tokenizer = AutoTokenizer.from_pretrained(model_id)
Define tools
tools = [{
"type": "function",
"function": {
"name": "query_daily_sales_report",
"description": "Connects to a database to retrieve overall sales volumes and sales information for a given day.",
"parameters": {
"type": "object",
"properties": {
"day": {
"description": "Retrieves sales data for this day, formatted as YYYY-MM-DD.",
"type": "string",
}
},
"required": ["day"],
},
},
}]Define conversation input
conversation = [
{"role": "user", "content": "Can you provide a sales summary for 29th September 2023?"}
]Tokenize the Tool Use prompt directly
input_ids = tokenizer.apply_chat_template(
conversation=conversation,
tools=tools,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
)You can then generate from this input as normal.
If the model generates a plan and tool calls, you should add them to the chat history like so:
tool_call = {"name": "query_daily_sales_report", "arguments": {"day": "2023-09-29"}}
thinking = "I will use the query_daily_sales_report tool to find the sales summary for 29th September 2023."
conversation.append({"role": "assistant", "tool_calls": [{"id": "0", "type": "function", "function": tool_call}], "thinking": thinking})and then call the tool and append the result, as a dictionary, with the tool role, like so:
api_response_query_daily_sales_report = {"date": "2023-09-29", "summary": "Total Sales Amount: 10000, Total Units Sold: 250"} # this needs to be a dictionary!!Append tool results
conversation.append({"role": "tool", "tool_call_id": "0", "content": api_response_query_daily_sales_report})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.
Tool Use With Citations \[CLICK TO EXPAND\]
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:
On 29th September 2023, the total sales amount was <co>10000</co: 0:[0]> and the total units sold were <co>250.</co: 0:[0]>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
For errors or additional questions about details in this model card, contact \[labs@cohere.com\].
Try it now:
You can try Command A+ in the playground. You can also use it in our dedicated Hugging Face Space.
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Model size
219B params
Tensor type
BF16
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