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README.md
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@@ -21,7 +21,7 @@ We believe the future of AI is open. That’s why we’re sharing our latest mod
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- **Use or build optimized foundation models**, including Llama, Mistral, Qwen, Gemma, DeepSeek, and others, tailored for performance and accuracy in real-world deployments.
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- **Customize and fine-tune models for your workflows**, from experimentation to production, with tools and frameworks built to support reproducible research and enterprise AI pipelines.
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- **Maximize inference efficiency across hardware** using production-grade compression and optimization techniques like quantization (FP8, INT8, INT4), structured/unstructured sparsity, distillation, and more, ready for cost-efficient deployments with vLLM.
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- **Confidently deploy validated models** on Red Hat AI products.
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🔗 **Explore relevant open-source tools**:
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- [**vLLM**](https://github.com/vllm-project/vllm) – Serve large language models efficiently across GPUs and environments.
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- **Use or build optimized foundation models**, including Llama, Mistral, Qwen, Gemma, DeepSeek, and others, tailored for performance and accuracy in real-world deployments.
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- **Customize and fine-tune models for your workflows**, from experimentation to production, with tools and frameworks built to support reproducible research and enterprise AI pipelines.
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- **Maximize inference efficiency across hardware** using production-grade compression and optimization techniques like quantization (FP8, INT8, INT4), structured/unstructured sparsity, distillation, and more, ready for cost-efficient deployments with vLLM.
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- **Confidently deploy [validated models](http://www.redhat.com/en/products/ai/validated-models)** on Red Hat AI products.
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🔗 **Explore relevant open-source tools**:
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- [**vLLM**](https://github.com/vllm-project/vllm) – Serve large language models efficiently across GPUs and environments.
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