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Add SuperCompress to Pre-trained Models and Inference

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arjunkshah21 2026-06-28 21:32:50 -07:00
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@ -167,6 +167,7 @@ _Libraries for building AI applications, LLM integrations, and autonomous agents
- [transformers](https://github.com/huggingface/transformers) - A framework that lets you easily use pre-trained transformer models for NLP, vision, and audio tasks.
- [unsloth](https://github.com/unslothai/unsloth) - A library for faster LLM fine-tuning and training with reduced memory usage.
- [vllm](https://github.com/vllm-project/vllm) - A high-throughput and memory-efficient inference and serving engine for LLMs.
- [SuperCompress](https://supercompress.vercel.app) - Learned prompt compression that reduces LLM token costs by ~65% using a ~5K-param CPU policy (~60ms latency), preserving answer quality. Open-source (MIT).
- Speech
- [openai-whisper](https://github.com/openai/whisper) - A general-purpose automatic speech recognition model trained on 680k hours of multilingual and multitask supervised data.
- [funasr](https://github.com/modelscope/FunASR) - Industrial-grade speech recognition toolkit with 170x realtime speed, 50+ languages, speaker diarization, and emotion detection.