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README.md
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license: mit
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datasets:
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- LDJnr/Capybara
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pipeline_tag: text-generation
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tags:
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- llama
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### Fine-tuned [Biggie-SmoLlm-0.15B-Base](https://huggingface.co/nisten/Biggie-SmoLlm-0.15B-Base) for generating subqueries
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This dude is trained for boosting the performance of your IR app, or RAG
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My motivation was to tackle a core problem of IR with an extremely lightweight, but capable model.
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If queries are
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- vague, ask follow up questions
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- multiple sub questions, generate multiple queries for each of them
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Heads up: [Ollama](https://ollama.com/andthattoo/subquery-smollm) version works 160 tps on 1 CPU core. No GPU? No worries. This little dude’s got you.
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license: mit
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datasets:
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- LDJnr/Capybara
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- andthattoo/subqueries
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pipeline_tag: text-generation
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tags:
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- llama
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### Fine-tuned [Biggie-SmoLlm-0.15B-Base](https://huggingface.co/nisten/Biggie-SmoLlm-0.15B-Base) for generating subqueries
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This dude is trained for boosting the performance of your IR app, or RAG.
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+
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My motivation was to tackle a core problem of IR with an extremely lightweight, but capable model.
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If queries are
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- vague, ask follow up questions
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- multiple sub questions, generate multiple queries for each of them
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Training data was generated wit [Dria](dria.co). A decentralized p2p network for synthetic data. Join [discord](https://discord.gg/dria) to help decentralized data generation
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Heads up: [Ollama](https://ollama.com/andthattoo/subquery-smollm) version works 160 tps on 1 CPU core. No GPU? No worries. This little dude’s got you.
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