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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)
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- fine-tuned on synthetic data and optimized to run locally on
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- resource-constrained devices such as smartphones and CPUs. Teapot is trained
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- to only answer using context from documents, reducing hallucinations. Teapot
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- can perform a variety of tasks, including hallucination-resistant Question
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- 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
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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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  - text: >-
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- Teapot is an open-source small language model (~800 million parameters)
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- fine-tuned on synthetic data and optimized to run locally on
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- resource-constrained devices such as smartphones and CPUs. Teapot is trained
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- to only answer using context from documents, reducing hallucinations. Teapot
36
- can perform a variety of tasks, including hallucination-resistant Question
37
- Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
38
- 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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  - text: >-
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- Teapot is an open-source small language model (~800 million parameters)
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- fine-tuned on synthetic data and optimized to run locally on
49
- resource-constrained devices such as smartphones and CPUs. Teapot is trained
50
- to only answer using context from documents, reducing hallucinations. Teapot
51
- can perform a variety of tasks, including hallucination-resistant Question
52
- 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
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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.
57
 
58
 
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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)
63
- fine-tuned on synthetic data and optimized to run locally on
64
- resource-constrained devices such as smartphones and CPUs. Teapot is trained
65
- to only answer using context from documents, reducing hallucinations. Teapot
66
- can perform a variety of tasks, including hallucination-resistant Question
67
- Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
68
- TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data
69
- 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.
72
 
73
 
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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:
16
  - text: >-
17
+ 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.
18
+ Teapot is trained to only answer using context from documents, reducing hallucinations.
19
+ Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
20
+ TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3
21
+ TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi.
22
+ Teapot is a model built by and for the community.
 
 
 
 
23
 
24
 
25
  What devices can teapot run on?
26
  example_title: Question Answering
27
  - text: >-
28
+ 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.
29
+ Teapot is trained to only answer using context from documents, reducing hallucinations.
30
+ Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
31
+ TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data generated by Deepseek v3
32
+ TeapotLLM can be hosted on low-power devices with as little as 2GB of CPU RAM such as a Raspberry Pi.
33
+ Teapot is a model built by and for the community.
 
 
 
 
34
 
35
 
36
  Tell me about teapotllm
37
  example_title: Summarization Answering
38
  - text: >-
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.
 
 
 
 
45
 
46
 
47
  Extract the number of parameters
48
  example_title: Information Extraction
49
  - text: >-
50
+ 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.
55
+ Teapot is a model built by and for the community.
 
 
 
 
56
 
57
 
58
  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