command-a-plus-05-2026-w4a4

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What command-a-plus-05-2026-w4a4 is

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 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. As a result, you should get an output that looks like this, where the thinking is generated between the <STARTTHINKING and <ENDTHINKING: You can also use the model directly using transformers pipeline abstraction: vLLM -- Special instructions for W4A4 Command A+ w4a4 can only run on vLLM =0.25.0. W4A4 and accurate response parsing also requires installing Cohere’s melody library. Then the vllm server can be started with the following command: We recommend using the following set of sampling parameters for generation: temperature=0.9, topp=0.95, repetitionpenalty=1.04. Quantization Methodology: Reasoning models pay an outsized quantization tax: long decoding traces compound per-token errors, so naive low-bit conversion typically shows up as visible regressions on hard benchmarks. To mitigate this, we quantize selectively and use distillation to close the residual quality gap. We apply NVFP4 W4A4 quantization (4-bit weights and activations, with two-level scaling) to the MoE experts only. T

Summarised from the published model card. Read the full card on the HuggingFace links below.

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Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

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Call it like any OpenAI endpoint

Once AxForge deploys command-a-plus-05-2026-w4a4 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (command-a-plus-05-2026-w4a4 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"command-a-plus-05-2026-w4a4","messages":[{"role":"user","content":"Hello"}]}'

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