Mpt 7B 64K Context Size Tokens Trained Open Source Llm And Chatgpt Gpt4 With Code İnterpreter, youtube mp3 indir
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MPT-7B 64K+ Context Size / Tokens Trained Open Source LLM and ChatGPT / GPT4 with Code Interpreter
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0:00 Introduction to Longgboi of #MosaicML and Code Interpreter of #ChatGPT
0:26 Announcement of Naveen Rao, who is CEO of MosaicML
0:38 What is context length / token size of ChatGPT
0:53 What does context length / token size do
1:30 What can you do with 64k context length
1:57 What is ChatGPT / GPT4 Code Interpreter
2:41 Example of Python code execution by ChatGPT
3:19 ChatGPT Code Interpreter is asked with show me something numinous using Python
3:41 Uploading excel file without context and asking questions to GPT4 with Code Interpreter
4:25 Uploading 60 mb US Census data having excel file to ChatGPT with Code Interpreter
5:15 GPT4 has all kind of data visualization capabilities with code interpreter
5:30 GPT4 with plugins and browsers
5:49 AI reads entire epilogue with my trained voice
MPT-7B, the latest entry in our MosaicML Foundation Series. MPT-7B is a transformer trained from scratch on 1T tokens of text and code. It is open source, available for commercial use, and matches the quality of LLaMA-7B. MPT-7B was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k. Starting today, you can train, finetune, and deploy your own private MPT models, either starting from one of our checkpoints or training from scratch. For inspiration, we are also releasing three finetuned models in addition to the base MPT-7B: MPT-7B-Instruct, MPT-7B-Chat, and MPT-7B-StoryWriter-65k+, the last of which uses a context length of 65k tokens!
The Rise of Large Language Models and their Impact on Text Generation
Introduction:
Language models are computer programs designed to analyze, process and generate language. With the emergence of machine learning and artificial intelligence, these models have become increasingly sophisticated and capable of generating human-like language. Large language models have become particularly popular, due to their ability to process vast amounts of data, and generate coherent and natural language.
What are Large Language Models?
Large language models (LLMs) are deep learning models that are trained on massive amounts of text data. These models are designed to understand the structure of language and the context in which it is used. They work by analyzing the patterns in text data and learning to predict the likelihood of certain words or phrases appearing in a given context.
Context Size:
Context size refers to the amount of text data that a language model processes in order to generate language. The larger the context size, the more accurate and coherent the language generated by the model is likely to be. This is because larger context sizes allow the model to understand the nuances of language better, and to generate more complex and nuanced responses.
Effect of Context Size:
The effect of context size on language generation has been studied extensively, and it has been found that larger context sizes result in more accurate and coherent language generation. This is particularly important when it comes to generating longer pieces of text, such as articles or essays.
Voice Cloning:
Voice cloning is the process of creating a digital replica of a person's voice. This is achieved by training a deep learning model on samples of the person's voice, and then using the model to generate new audio that sounds like the person speaking. Voice cloning has numerous applications, including in the entertainment industry, where it can be used to create digital versions of actors or musicians.
Deep Voice Cloning:
Deep voice cloning refers to the use of deep learning models to create highly realistic and accurate voice clones. These models are trained on vast amounts of audio data, and are capable of generating audio that is almost indistinguishable from the original. Deep voice cloning has many potential applications, including in