fix smolagent

docs/blog/posts/Smolagent.md

@@ -8,6 +8,8 @@ slug: smolagent
 title: SmolAgentXEmbedAnything
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 When working with domain-specific queries, we often struggle with the challenge of balancing in-domain and out-of-domain requests. But not anymore! With **embedanything**, you can leverage fine-tuned, domain-focused models while **smolagent** takes the lead in smart decision-making. Whether you're handling queries from different domains or need to combine their insights seamlessly, smolagent ensures smooth collaboration, merging responses for a unified, accurate answer.
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 But first let’s discuss what is SmolAgent and then we can discuss each retrieval :
 

docs/blog/posts/v0.5.md

@@ -12,6 +12,9 @@ We are thrilled to share that EmbedAnything version 0.5 is out now and comprise
 
 The best of all have been support for late-interaction model, both ColPali and ColBERT on onnx.
 
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 1. **ModernBert** Support: Well it made quite a splash, and we were obliged to add it, in the fastest inference engine, embedanything. In addition to being faster and more accurate, ModernBERT also increases context length to 8k tokens (compared to just 512 for most encoders), and is the first encoder-only model that includes a large amount of code in its training data.
 2. **ColPali- Onnx** :  Running the ColPali model directly on a local machine might not always be feasible. To address this, we developed a **quantized version of ColPali**. Find it on our hugging face, link [here](https://huggingface.co/starlight-ai/colpali-v1.2-merged-onnx). You could also run it both on Candle and on ONNX.
 3. **ColBERT**: ColBERT is a *fast* and *accurate* retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds.