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@@ -26,7 +26,7 @@ We believe the future of AI is open. That’s why we’re sharing our latest mod
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  πŸ”— **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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  - [**LLM Compressor**](https://github.com/vllm-project/llm-compressor) – Compress and optimize your own models with SOTA quantization and sparsity techniques.
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- - [**Speculators**](https://github.com/vllm-project/speculators) – A unified library for building, evaluating, and storing speculative decoding algorithms for LLM inference in vLLM.
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  - [**InstructLab**](https://github.com/instructlab) – Fine-tune open models with your data using scalable, community-backed workflows.
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  - [**GuideLLM**](https://github.com/neuralmagic/guidellm) – Benchmark, evaluate, and guide your deployments with structured performance and latency insights.
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  πŸ”— **Explore relevant open-source tools**:
27
  - [**vLLM**](https://github.com/vllm-project/vllm) – Serve large language models efficiently across GPUs and environments.
28
  - [**LLM Compressor**](https://github.com/vllm-project/llm-compressor) – Compress and optimize your own models with SOTA quantization and sparsity techniques.
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+ - [**Speculators**](https://github.com/vllm-project/speculators) – Build, evaluate, and store speculative decoding algorithms for LLM inference in vLLM.
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  - [**InstructLab**](https://github.com/instructlab) – Fine-tune open models with your data using scalable, community-backed workflows.
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  - [**GuideLLM**](https://github.com/neuralmagic/guidellm) – Benchmark, evaluate, and guide your deployments with structured performance and latency insights.
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