txtai

6 ms·

1.
by txtai 3y ago
Excellent work! Hope to see a version that has a friendlier commercial license in the future (current version is CC-BY 4.0 NC).
2.
by txtai 3y ago
If you want something similar to this that is fully downloadable and open source, check this out - https://huggingface.co/NeuML/txtai-wikipedia It's an embeddings database of Wikipedia abstracts with page view dat
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by txtai 3y ago
SetFit is a great framework for building a text classifier. This is a pretty straight forward problem and a good fit for a standard text classifier as well. Here is an example of fine-tuning a model with txtai: https://colab.rese
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by txtai 3y ago
txtai combines Faiss and SQLite to support similarity search with SQL. For example: SELECT id, text, date FROM txtai WHERE similar('machine learning') AND date >= '2023-03-30' GitHub: https://github.com&#x2
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by txtai 3y ago
txtai combines SQLite and Faiss to enable vector search. It also does a lot more than that. https://github.com/neuml/txtai
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by txtai 4y ago
The first comment in that article has details on the new model. Not the original author but per their testing they said they paid $70 to encode 1M records. The embeddings are 1536 dimensions, which require a lot of vector storage. The HF hu
7.
by txtai 4y ago
That's up to you. Many don't want to open an account and pay in order to explore what's possible. There are LLMs available on the HF Hub, such as google/flan-t5-xl.
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by txtai 4y ago
Starting with HF models and moving to a large model like GPT3 when the task calls for it is a good approach to take with almost all tasks.
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by txtai 4y ago
If speed and price are concerns, use the FOSS models available on the Hugging Face Hub: https://hf.co/models . Thousands of models, different sizes and tasks. Download locally and fine-tune, if necessary. For those specifica
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by txtai 4y ago
Thanks. Just want people to know there are quality FOSS alternatives available for encoding text into embeddings.
11.
by txtai 4y ago
txtai is an alternative approach to this. It builds a FAISS (also supports HNSW) index alongside a SQLite database. It works with sentence-transformers models. For example, this model https://huggingface.co/sentence-transfor
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by txtai 4y ago
This is a really nice article by Dmitry Kan on number of popular vector databases. https://towardsdatascience.com/milvus-pinecone-vespa-weaviat...
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by txtai 4y ago
This concept was more for inference on-demand vs training a model. For example, if there was an API call that had a model call as part of it's workflow. Training even a small model still requires serious compute power.
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by txtai 4y ago
There is also a push to run machine learning models on low-resource devices, which a $4 VPS could be categorized as. https://www.tensorflow.org/lite https://huggingface.co/muhtasham/olm-bert-tiny-decemb
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by txtai 4y ago
There is a GitHub repo: https://github.com/amazon-science/mm-cot But it doesn't look all that easy to stand up.
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by txtai 4y ago
InstructGPT which is a "sibling" model to ChatGPT is 1.3B parameters. https://openai.com/blog/instruction-following/ Another thread on HN ( https://news.ycombinator.com/item?id=34653075 )
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by txtai 4y ago
Great looking project here. Absolutely need a local/FOSS option. There's been a number of open-source libraries for LLMs lately that simply call into paid/closed models via APIs. Not exactly the spirit of open-source. There&#
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by txtai 4y ago
Outperforms GPT-3.5 by 16% with less than 1B parameters. There is even a 220M parameter version that scores well. Interesting model to watch. Referenced snippet from the abstract: With Multimodal-CoT, our model under 1 billion parameters ou
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by txtai 4y ago
GitHub repo: https://github.com/neuml/txtai
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by txtai 4y ago
The goal of this approach is to answer questions with a datasource. The referenced example runs a vector database query to build the context and then runs a prompt to ask the question. Answer the following question using only the context be
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by txtai 4y ago
This example isn't using gpt embeddings but it could. It's using local Hugging Face models (FLAN-T5) with a context provided by a vector store (Faiss).
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by txtai 4y ago
Very interesting! Looking forward to seeing more embedded AI devices coming out. More NLP based, but here is an article on an effort to build Transformers micromodels to run on embedded devices. The model in this example is under 1MB. Goal
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by txtai 4y ago
Link to example: https://neuml.hashnode.dev/prompt-driven-search-with-llms
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Prompt-driven vector search with LLMs(github.com)
28 points by txtai 4y ago|5 comments
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by txtai 4y ago
Article also available: https://neuml.hashnode.dev/prompt-driven-search-with-llms
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Parse research papers into a structured dataset(github.com)
3 points by txtai 4y ago|0 comments
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Show HN: ETL for Medical and Scientific Papers(github.com)
2 points by txtai 4y ago|0 comments
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Show HN: Semantic search and workflows for medical/scientific papers(github.com)
12 points by txtai 4y ago|0 comments
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by txtai 4y ago
Also available as an article: https://neuml.hashnode.dev/train-a-language-model-from-scrat...
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Train a Transformers Micromodel: 400KB, 95K parameters(colab.research.google.com)
3 points by txtai 4y ago|1 comments

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