Nice job!
I gave it this image and asked it to be short and clear and summarize what's on the image. Did well in chinese!
But the same request in English - I got a looong response! Three Final Answers!!... I suppose it's a Chinese first model?
Ngodi
mrngodi
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AI & ML interests
RNN, CNN, LLM, LVM,
Recent Activity
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JustinLin610's
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about 1 month ago
Finally, Qwen1.5-110B is out! With weights and demo!
Blog: https://qwenlm.github.io/blog/qwen1.5-110b/
Demo: https://huggingface.co/spaces/Qwen/Qwen1.5-110B-Chat-demo
Base: https://huggingface.co/Qwen/Qwen1.5-110B
Chat: https://huggingface.co/Qwen/Qwen1.5-110B-Chat
This model has some specific features:
* GQA
* 32K token context length
* Multilingual support
We feel good about its performance on benchmarks, including those for base models and chat models, but we still need more of your testing and feedback to help us know its capabilities and limitations!
Additionally, the base model has not learned chatml tokens. Yeah if you use chatml format, you need to be careful about it!
Enjoy and stay tuned for Qwen2!
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mrngodi's activity
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about 1 month ago
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A complete RAG pipeline includes a reranker, which ranks the documents to find the best document 📓
Same goes for multimodal RAG, multimodal rerankers which we can integrate to multimodal RAG pipelines!
Learn how to build a complete multimodal RAG pipeline with vidore/colqwen2-v1.0 as retriever, lightonai/MonoQwen2-VL-v0.1 as reranker, Qwen/Qwen2-VL-7B-Instruct as VLM in this notebook that runs on a GPU as small as L4 🔥 https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_reranker_and_vlms
Same goes for multimodal RAG, multimodal rerankers which we can integrate to multimodal RAG pipelines!
Learn how to build a complete multimodal RAG pipeline with vidore/colqwen2-v1.0 as retriever, lightonai/MonoQwen2-VL-v0.1 as reranker, Qwen/Qwen2-VL-7B-Instruct as VLM in this notebook that runs on a GPU as small as L4 🔥 https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_reranker_and_vlms
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