fzliu

22 ms·

331.
Elasticsearch vs. Vespa Performance Comparison(blog.vespa.ai)
1 points by fzliu 2y ago|0 comments
332.
Getting started with Voyage multimodal embeddings(colab.research.google.com)
1 points by fzliu 2y ago|0 comments
333.
Multimodal embeddings for interleaved text and images(docs.voyageai.com)
1 points by fzliu 2y ago|0 comments
334.
Adaptive Length Image Tokenization via Recurrent Allocation(arxiv.org)
1 points by fzliu 2y ago|0 comments
335.
An Empirical Model of Large-Batch Training (2018)(arxiv.org)
1 points by fzliu 2y ago|0 comments
336.
Elasticsearch vs. Vespa Performance Comparison(blog.vespa.ai)
5 points by fzliu 2y ago|0 comments
337.
Embedding Model Benchmark Tool(github.com)
2 points by fzliu 2y ago|0 comments
338.
How Does a Reranker Work?(blog.voyageai.com)
1 points by fzliu 2y ago|0 comments
339.
Prejudice and China(research.gavekal.com)
4 points by fzliu 2y ago|1 comments
340.
Rerank-2 and rerank-2-lite: the next generation of Voyage multilingual rerankers(blog.voyageai.com)
1 points by fzliu 2y ago|0 comments
341.
Multimodal embeddings for interleaved text and images(docs.voyageai.com)
1 points by fzliu 2y ago|0 comments
342.
Double Delusion(lichess.org)
3 points by fzliu 2y ago|0 comments
343.
Understanding Warmup-Stable-Decay Learning Rates(arxiv.org)
1 points by fzliu 2y ago|0 comments
344.
Multimodal embeddings for interleaved text and images(docs.voyageai.com)
3 points by fzliu 2y ago|0 comments
345.
by fzliu 2y ago
We provide retrieval metrics for a variety of datasets and languages: https://blog.voyageai.com/2024/09/18/voyage-3/ . I also personally encourage folks to either test on their own data or to find an open
346.
by fzliu 2y ago
Great post! One quick minor note is that the resulting embeddings for the same text string could be different, depending on what you specify the input type as for retrieval tasks (i.e. query or document) -- check out the `input_type` parame
347.
by fzliu 2y ago
(I work at Voyage) Many of the top-performing models that you see on the MTEB retrieval for English and Chinese tend to overfit to the benchmark nowadays. voyage-3 and voyage-3-lite are also pretty small in size compared to a lot of the 7B
348.
What the Hype and Reality of Agents(leehanchung.github.io)
2 points by fzliu 2y ago|0 comments
349.
Multimodal RAG(github.com)
1 points by fzliu 2y ago|1 comments
350.
32K-token context length embedding models(blog.voyageai.com)
1 points by fzliu 2y ago|0 comments
351.
How to win Equalized/Worst positions?(lichess.org)
1 points by fzliu 2y ago|0 comments
352.
The Modern RAG Stack(adamkhakhar.substack.com)
5 points by fzliu 2y ago|1 comments
353.
OmniParser for Pure Vision Based GUI Agent(microsoft.github.io)
145 points by fzliu 2y ago|34 comments
354.
Improving Generalization Performance by Switching from Adam to SGD (2017)(arxiv.org)
1 points by fzliu 2y ago|0 comments
355.
Embeddings Drive the Quality of RAG: A Case Study of Chat.LangChain(blog.voyageai.com)
1 points by fzliu 2y ago|0 comments
356.
Rerank-2 and rerank-2-lite: the next generation of Voyage multilingual rerankers(blog.voyageai.com)
1 points by fzliu 2y ago|0 comments
357.
Voyage-3 and voyage-3-lite: small yet mighty general-purpose embedding models(blog.voyageai.com)
1 points by fzliu 2y ago|0 comments
358.
Why opening statistics are hard(lichess.org)
2 points by fzliu 2y ago|0 comments
359.
Does Refusal Training in LLMs Generalize to the Past Tense?(arxiv.org)
2 points by fzliu 2y ago|0 comments
360.
TxT360(huggingface.co)
2 points by fzliu 2y ago|0 comments

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