To this end, we release OpenELM, a state-of-the-art open language model.
We present BenchmarkName, a novel benchmark to quantify LLM security risks and capabilities.
Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99. 3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU.
Finally, we present a customization method using a pair of person-garment images, which significantly improves fidelity and authenticity.
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Based on this pipeline, a random face reference training method is further devised to precisely capture the ID-relevant embeddings from reference images, thus improving the fidelity and generalization capacity of our model for ID-specific video generation.
Applying Reinforcement Learning (RL) to sequence generation models enables the direct optimization of long-term rewards (\textit{e. g.,} BLEU and human feedback), but typically requires large-scale sampling over a space of action sequences.
We propose a new metric to assess personality generation capability based on this evaluation method.
We propose AutoCrawler, a two-stage framework that leverages the hierarchical structure of HTML for progressive understanding.
We present InstantMesh, a feed-forward framework for instant 3D mesh generation from a single image, featuring state-of-the-art generation quality and significant training scalability.
Specifically, SnapKV achieves a consistent decoding speed with a 3. 6x increase in generation speed and an 8. 2x enhancement in memory efficiency compared to baseline when processing inputs of 16K tokens.