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README.md
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Teapot is an open-source small language model (~800 million parameters)
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can
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TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data
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generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with
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as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built
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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)
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can
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TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data
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generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with
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as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built
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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)
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can
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TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data
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generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with
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as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built
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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)
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can
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TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data
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generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with
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as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built
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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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- transformers.js
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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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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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23 |
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What devices can teapot run on?
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example_title: Question Answering
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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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30 |
+
Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
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31 |
+
TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3
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32 |
+
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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33 |
+
Teapot is a model built by and for the community.
|
|
|
|
|
|
|
|
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34 |
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35 |
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Tell me about teapotllm
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example_title: Summarization Answering
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- text: >-
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39 |
+
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.
|
40 |
+
Teapot is trained to only answer using context from documents, reducing hallucinations.
|
41 |
+
Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
|
42 |
+
TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3
|
43 |
+
TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi.
|
44 |
+
Teapot is a model built by and for the community.
|
|
|
|
|
|
|
|
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45 |
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Extract the number of parameters
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example_title: Information Extraction
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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.
|
51 |
+
Teapot is trained to only answer using context from documents, reducing hallucinations.
|
52 |
+
Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
|
53 |
+
TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3
|
54 |
+
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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|
|
|
|
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|
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56 |
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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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