Models & Labs
Model-Preserving Adaptive Rounding Introduced
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Why it matters
- →This development could enhance the efficiency of deploying machine learning models in resource-constrained environments.
Together AI has announced a new technique called Model-Preserving Adaptive Rounding (YAQA) aimed at improving model performance during quantization. This method seeks to maintain the integrity of machine learning models while reducing their size.
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Together AI Partners with Adaption
Together AI and Adaption have formed a partnership to integrate Together Fine-Tuning into Adaptive Data, enabling teams to optimize datasets and deploy stronger open models.
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Together AI addresses Copy Fail vulnerability
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The latest version b8991 of llama.cpp has been released, featuring updates for various operating systems.
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llama.cpp update enhances compatibility and performance
The latest update to llama-mmap improves compatibility with various platforms and model sizes. Key enhancements include support for 32-bit wasm and updates to gguf.cpp style.
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