https://huggingface.co/IntelLabs/LlavaOLMoBitnet1B\n","updatedAt":"2024-08-27T03:21:24.432Z","author":{"_id":"60f1abe7544c2adfd699860c","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg","fullname":"AK","name":"akhaliq","type":"user","isPro":false,"isHf":true,"isHfAdmin":false,"isMod":false,"followerCount":9179,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.6736628413200378},"editors":["akhaliq"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg"],"reactions":[],"isReport":false}},{"id":"66ce7e70e06625ccdaf3e6ab","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":318,"isUserFollowing":false},"createdAt":"2024-08-28T01:33:36.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [CROME: Cross-Modal Adapters for Efficient Multimodal LLM](https://huggingface.co/papers/2408.06610) (2024)\n* [LLAVADI: What Matters For Multimodal Large Language Models Distillation](https://huggingface.co/papers/2407.19409) (2024)\n* [SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs](https://huggingface.co/papers/2408.11813) (2024)\n* [IAA: Inner-Adaptor Architecture Empowers Frozen Large Language Model with Multimodal Capabilities](https://huggingface.co/papers/2408.12902) (2024)\n* [Instruction Tuning-free Visual Token Complement for Multimodal LLMs](https://huggingface.co/papers/2408.05019) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
\nThe following papers were recommended by the Semantic Scholar API
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- CROME: Cross-Modal Adapters for Efficient Multimodal LLM (2024) \n
- LLAVADI: What Matters For Multimodal Large Language Models Distillation (2024) \n
- SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs (2024) \n
- IAA: Inner-Adaptor Architecture Empowers Frozen Large Language Model with Multimodal Capabilities (2024) \n
- Instruction Tuning-free Visual Token Complement for Multimodal LLMs (2024) \n
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Abstract
LLaVaOLMoBitnet1B is the first open-source ternary multimodal LLM that processes images and text to generate coherent responses, designed to run efficiently on small computational resources.
AI-generated summary
Multimodal Large Language Models (MM-LLMs) have seen significant advancements in the last year, demonstrating impressive performance across tasks. However, to truly democratize AI, models must exhibit strong capabilities and be able to run efficiently on small compute footprints accessible by most. Part of this quest, we introduce LLaVaOLMoBitnet1B - the first Ternary Multimodal LLM capable of accepting Image(s)+Text inputs to produce coherent textual responses. The model is fully open-sourced along with training scripts to encourage further research in this space. This accompanying technical report highlights the training process, evaluation details, challenges associated with ternary models and future opportunities. Link to the model: https://huggingface.co/IntelLabs/LlavaOLMoBitnet1B
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This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- CROME: Cross-Modal Adapters for Efficient Multimodal LLM (2024)
- LLAVADI: What Matters For Multimodal Large Language Models Distillation (2024)
- SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs (2024)
- IAA: Inner-Adaptor Architecture Empowers Frozen Large Language Model with Multimodal Capabilities (2024)
- Instruction Tuning-free Visual Token Complement for Multimodal LLMs (2024)
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