Update README via Huggy

README.md

@@ -67,17 +67,16 @@ Command A+ is an open source model with 25 billion active parameters and 218B to
 
 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  
+* 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). 
+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
@@ -90,8 +89,7 @@ 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](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). 
-
+For more details, please check out our [blog post](http://cohere.com/blog/command-a-plus).
 
 **Usage**
 
@@ -117,9 +115,9 @@ input_ids = tokenizer.apply_chat_template(
 )
 
 gen_tokens = model.generate(
-    input_ids, 
-    max_new_tokens=4096, 
-    do_sample=True, 
+    input_ids,
+    max_new_tokens=4096,
+    do_sample=True,
     temperature=0.6,
     top_p=0.95
 )
@@ -171,10 +169,10 @@ 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/). 
+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 vllm>=0.21.0
 uv pip install transformers uv pip install cohere_melody>=0.9.0
 ```
 
@@ -188,9 +186,9 @@ Then the vllm server can be started with the following command:
 
 **Input**: Text and images.
 
-**Output**: Model generates text. 
+**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. 
+**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.
 
@@ -289,5 +287,3 @@ For errors or additional questions about details in this model card, contact \[[
 **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).
-
-