ppo-Huggy
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From language to images, open weights to APIs. Understand capabilities and compare real costs.
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1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation 7. How To Get Started With the Model A CLIP ViT-H/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). As per the original OpenAI CLIP model card, this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model.
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GGUF quantizations of Aleph-Alpha/Kolibri-1, a 78B-parameter Mixture-of-Experts reasoning model (3.46B active per token) for German and English, released by Aleph Alpha under Apache-2.0. This is an independent conversion. It is not made or endorsed by Aleph Alpha or the llama.cpp project. For the split files (Q5_K_M, Q6_K, Q8_0), point llama.cpp at the first part. The second part loads automatically.
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A custom hybrid version of **MiniMax-H3** combining the **FL2VA** and **REF2VA** branches of the model. This hybrid layout was constructed at the raw safetensor payload level. No tensor conversion was performed when constructing the BF16 hybrid. The B25-B49 tensors were copied byte-for-byte from the REF2VA ZS05 BF16 model, while the remaining tensors come from the FL2VA ZS05 BF16 model.
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