Emu3-Chat
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Emu3-Chat-hf
BAAIBelow is the model card of Emu3-Chat model, which is adapted from the original Emu3 model card that you can find here. **Model type:** Emu3 is an open-source multimodal models trained with next-token prediction task. By tokenizing images and text into a discrete space, Emu3 is trained as a single transformer from scratch on a mixture of multimodal sequences. It is an auto-regressive language model, based on the transformer architecture. - **Emu3** is capable of generating high-quality images following the text input, by simply predicting the next vision token. The model naturally supports flexible resolutions and styles. - **Emu3** shows strong vision-language understanding capabilities to see the physical world and provides coherent text responses. Notably, this capability is achieved without depending on a CLIP and a pretrained LLM. - **Emu3** simply generates a video causally by predicting the next token in a video sequence, unlike the video diffusion model as in Sora. With a video in context, Emu3 can also naturally extend the video and predict what will happen next. - **Emu3** outperforms several well-established task-specific models in both generation and perception tasks, surpassing flagship open models such as SDXL, LLaVA-1.6 and OpenSora-1.2, while eliminating the need for diffusion or compositional architectures.
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View details ↗Emu3-Gen
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View details ↗Emu3-Gen-hf
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View details ↗Emu3-Stage1
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View details ↗EVA
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View details ↗EVA-CLIP-18B
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View details ↗EVA-CLIP-8B
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View details ↗EVA-CLIP-8B-448
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View details ↗EVE-7B-HD-v1.0
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View details ↗EVE-7B-HD-v2.0
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View details ↗EVE-7B-v1.0
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View details ↗GaussianMind
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View details ↗Infinity-Instruct-3M-0625-Llama3-8B is an opensource supervised instruction tuning model without reinforcement learning from human feedback (RLHF). This model is just finetuned on Infinity-Instruct-3M and Infinity-Instruct-0625 and showing favorable results on AlpacaEval 2.0 and MT-Bench. Infinity-Instruct-3M-0625-Llama3-8B is tuned on Million-level instruction dataset Infinity-Instruct. First, we apply the foundational dataset Infinity-Instruct-3M to improve the foundational ability (math & code) of Llama3-8B, and get the foundational instruct model Infinity-Instruct-3M-Llama3-8B. Then we finetune the Infinity-Instruct-3M-Llama3-8B to get the stronger chat model Infinity-Instruct-3M-0625-Llama3-8B. Here is the training hyperparamers. Thanks to FlagScale, we could concatenate multiple training samples to remove padding token and apply diverse acceleration techniques to the traning procudure. It effectively reduces our training costs. We will release our code in the near future!
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