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README.md

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 ---
+inference: false
+base_model: CohereLabs/command-a-plus-05-2026
+library_name: transformers
+language:
+- en
+- ar
+- bg
+- bn
+- ca
+- cs
+- da
+- de
+- el
+- es
+- et
+- fa
+- fi
+- fil
+- fr
+- ga
+- he
+- hi
+- hr
+- hu
+- id
+- is
+- it
+- ja
+- ko
+- lt
+- lv
+- ms
+- mt
+- nl
+- 'no'
+- pa
+- pl
+- pt
+- ro
+- ru
+- sk
+- sl
+- sr
+- sv
+- ta
+- te
+- th
+- tr
+- uk
+- ur
+- vi
+- zh
 license: apache-2.0
+pipeline_tag: image-text-to-text
+tags:
+- conversational
+- chat
 ---
+
+## Command A+ GGUFs
+
+| Filename | Size (GB) |
+|---|---|
+| command-a-plus-05-2026-bf16.gguf | 407 |
+| command-a-plus-05-2026-q4_k_m.gguf | 124 |
+| command-a-plus-05-2026-q4_k_s.gguf | 116 |
+| command-a-plus-05-2026-q3_k_m.gguf | 98 |
+
+> **Note:** These GGUF files are text-only and do not support image input.
+
+---
+
+# **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](https://cohere.com/) and [Cohere Labs](https://cohere.com/research)
+
+* Point of Contact: [**Cohere Labs**](https://cohere.com/research)  
+* License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-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](http://cohere.com/blog/command-a-plus). 
+
+You can try out Command A+ before downloading the weights in our hosted [Hugging Face Space](https://huggingface.co/spaces/CohereLabs/command-a-plus-05-2026).
+
+
+**Available quantizations**
+
+The following quantizations are available with example minimum GPU requirements
+
+| Quantization | Blackwell | Hopper |
+| :---- | :---- | :---- |
+| [BF16 (16-bit)](https://huggingface.co/CohereLabs/command-a-plus-05-2026-bf16) | 4 x B200 | 8 x H100 |
+| [FP8 (8-bit)](https://huggingface.co/CohereLabs/command-a-plus-05-2026-fp8) | 2 x B200 | 4 x H100 |
+| [W4A4 (4-bit)](https://huggingface.co/CohereLabs/command-a-plus-05-2026-w4a4) | 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](https://huggingface.co/CohereLabs/command-a-plus-05-2026-w4a4) which boasts superior speed and latency characteristics alongside a smaller hardware footprint.**
+
+For more details, please check out our [blog post](http://cohere.com/blog/command-a-plus). 
+
+
+**Usage**
+
+**Transformers**
+
+Please install transformers from the source repository that includes the necessary changes for this model.
+
+```py
+# pip install transformers
+from transformers import AutoTokenizer, AutoModelForImageTextToText
+
+model_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>`:
+
+```py
+<|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:
+
+```py
+from transformers import pipeline
+import torch
+
+model_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](https://pypi.org/project/cohere-melody/). 
+
+```
+uv pip install vllm>=0.21.0 
+uv pip install transformers uv pip install cohere_melody>=0.9.0
+```
+
+Then 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-choice
+```
+
+## **Model 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](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
+
+model_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:
+
+```py
+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:
+
+```py
+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](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:
+
+```
+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 ",".
+</details>
+
+
+## **Model Card Contact**
+
+For errors or additional questions about details in this model card, contact \[[labs@cohere.com](mailto:labs@cohere.com)\].
+
+**Try it now:**
+
+You can try Command A+ in the [playground](https://dashboard.cohere.com/playground/chat?model=command-a-plus-05-2026). You can also use it in our dedicated [Hugging Face Space](https://huggingface.co/spaces/CohereLabs/command-a-plus-05-2026).

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