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README.md
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- text2text-generation
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- text: 'Translate to German: My name is Arthur'
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example_title: Translation
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Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs.
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Teapot is trained to only answer using context from documents, reducing hallucinations.
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TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi.
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Teapot is a model built by and for the community.
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example_title: Question Answering
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Answer the following yes/no question by reasoning step-by-step. Can you
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write a whole Haiku in a single tweet?
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example_title: Reasoning task
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- text: 'Q: ( False or not False or False ) is? A: Let''s think step by step'
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example_title: Boolean Expressions
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base_model:
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- google/flan-t5-large
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pipeline_tag: text2text-generation
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tags:
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- text2text-generation
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widget:
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- text: >-
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Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs.
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Teapot is trained to only answer using context from documents, reducing hallucinations.
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TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi.
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Teapot is a model built by and for the community.
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What devices can teapot run on?
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example_title: Question Answering
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Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs.
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Teapot is trained to only answer using context from documents, reducing hallucinations.
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Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
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TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3
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TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi.
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Teapot is a model built by and for the community.
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Tell me about teapotllm
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example_title: Summarization Answering
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Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs.
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Teapot is trained to only answer using context from documents, reducing hallucinations.
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Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
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TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3
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TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi.
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Teapot is a model built by and for the community.
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Extract the number of parameters
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example_title: Information Extraction
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Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs.
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Teapot is trained to only answer using context from documents, reducing hallucinations.
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Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
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TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3
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TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi.
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Teapot is a model built by and for the community.
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How many parameters is Deepseek?
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example_title: Hallucination Resistance
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base_model:
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- google/flan-t5-large
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pipeline_tag: text2text-generation
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