Customers have been inquiring about how to run a local LLM, a private one, for secrecy. In their servers at the local network, data center, or in a VM, on-premises. As a matter of fact, some companies are not keen to upload their knowledge base, and sensitive clients’ data to the cloud. Or just want to try more options. All those LLMs services online paid or free are all experimental. On another hand, to use LLMs adding enterprise data can be interesting and useful to automate business services, and create new services, bringing unexpected creative and innovative possibilities. Companies have terabytes of data, it would be hard to “upload” it to the LLM at cloud. One possibility we can try is to run the LLM close to the data.
To do a try, first I looked for the easiest way (low time and effort) to have an LLM locally, and so, I found this amazing Mozilla project llamafile!
https://github.com/Mozilla-Ocho/llamafile
You can just download the model you want and run it! Easy easy!!
https://github.com/Mozilla-Ocho/llamafile
You can just download the model you want and run it! Easy easy!!
I did a try with those two models:
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TinyLlama-1.1B (2.05 GB)
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llava-v1.5-7b-q4.llamafile (4.29 GB)
Of course there are different use cases for those simple and smaller models we can run in a PC, and from those advanced OpenAI GPT-4 bigdata models that runs at the cloud on powerful servers. We can combine both in our projects and with curated data from Wolfram|Alpha.
Here I am running in a pretty standard dev PC, Linux, i7, 32GB of memory, 3TB for data, NVIDIA CUDA...
In short, this is the way to setup and run the LLMs on Linux:
mkdir llm
cd llm
wget https://huggingface.co/Mozilla/llava-v1.5-7b-llamafile/resolve/main/llava-v1.5-7b-q4.llamafile?download=true
wget https://huggingface.co/Mozilla/TinyLlama-1.1B-Chat-v1.0-llamafile/resolve/main/TinyLlama-1.1B-Chat-v1.0.F16.llamafile?download=true
chmod +x *llamafile
./llava-v1.5-7b-q4.llamafile
cd llm
wget https://huggingface.co/Mozilla/llava-v1.5-7b-llamafile/resolve/main/llava-v1.5-7b-q4.llamafile?download=true
wget https://huggingface.co/Mozilla/TinyLlama-1.1B-Chat-v1.0-llamafile/resolve/main/TinyLlama-1.1B-Chat-v1.0.F16.llamafile?download=true
chmod +x *llamafile
./llava-v1.5-7b-q4.llamafile
You can just open the browser pointing to http://localhost:8080 and use the Llama LLM by a chat web GUI:
On another hand, REST JSON web API is available too, and we can call it from Wolfram Language.
Another convenient features is that the standard parameters for this local LLM are like those from OpenAI!
Another convenient features is that the standard parameters for this local LLM are like those from OpenAI!
To make it easy to send queries (prompts), this function llama handles the details:
In[]:=
llama[prompt_]:=Module[{result=URLRead[HTTPRequest["http://localhost:8080/v1/chat/completions",<|"Body"->ExportString["model"->"LLaMA_CPP","stream"->False,"temperature"->1,(*0..2*)"messages"->{<|"role"->"system","content"->"You are LLAMAfile, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."|>,<|"role"->"user","content"->prompt|>},"JSON"],"Headers"-><|"Content-Type"->"application/json","Authorization"->"Bearer no-key"|>|>]]},Dataset[ImportString[result["Body"],"RawJSON"]]];
The function llama returns a Dataset from the JSON messages, with lots of meta data along the text response:
llama["Tell me about the economics of Texas"]
Out[]=
The function llamaContent drills down the messages and returns just the text/content response we want, and returns it in Orange to highlight that those are LLM AI synthesis.
In[]:=
llamaContent[prompt_]:=CellPrint[Cell[StringReplace[First[Normal[llama[prompt]["choices"][All,"message"][All,"content"]]],"</s>"->""],"Text",Orange]];
With Mozilla llamafile in place, now lets try some prompts:
In[]:=
llamaContent["How LLMs AI works?"]
LLMs, or Large Language Models, are AI systems trained using massive amounts of data and sophisticated algorithms to understand and generate human-like language. These models operate by analyzing patterns in the training data, learning to predict the likelihood of specific words or phrases based on the context of the conversation.
During the training process, LLMs are exposed to massive datasets containing various types of text, including articles, books, emails, and social media posts. As the model processes this data, it learns to understand the structure and context of language, allowing it to generate coherent responses to user requests.
Once trained, LLMs can be fine-tuned to specific domains, applications, or even individual users, enhancing their performance and relevance in a particular context. Fine-tuning involves adjusting the model's parameters to adapt its understanding to the unique features and requirements of the target domain. This helps ensure that LLMs provide accurate and useful responses to user queries.
In conclusion, LLMs AI works by leveraging massive amounts of data and sophisticated algorithms to learn the nuances of human language, allowing it to generate meaningful and contextually relevant responses to user requests. Through fine-tuning, LLMs can be further customized to deliver even more precise and useful assistance.
During the training process, LLMs are exposed to massive datasets containing various types of text, including articles, books, emails, and social media posts. As the model processes this data, it learns to understand the structure and context of language, allowing it to generate coherent responses to user requests.
Once trained, LLMs can be fine-tuned to specific domains, applications, or even individual users, enhancing their performance and relevance in a particular context. Fine-tuning involves adjusting the model's parameters to adapt its understanding to the unique features and requirements of the target domain. This helps ensure that LLMs provide accurate and useful responses to user queries.
In conclusion, LLMs AI works by leveraging massive amounts of data and sophisticated algorithms to learn the nuances of human language, allowing it to generate meaningful and contextually relevant responses to user requests. Through fine-tuning, LLMs can be further customized to deliver even more precise and useful assistance.
In[]:=
llamaContent["Wha is the use case for GPT-4 and Llama AI LLM models?"]
GPT-4 and Llama AI LLM models are advanced language models designed to assist users with a wide range of natural language processing tasks. These models are primarily used to enhance various applications, such as chatbots, virtual assistants, language translation services, content generation, and more.
The use case for GPT-4 and Llama AI LLM models can be divided into a few key areas, such as:
1. Language Understanding: Improving the ability of the AI to understand and interpret natural language, allowing it to provide more accurate and relevant responses to user inputs.
2. Language Generation: Enhancing the AI's ability to generate coherent and context-appropriate text based on the user's request or input, whether it's a message, article, or any other form of written content.
3. Fine-tuning and Customization: GPT-4 and Llama AI LLM models can be fine-tuned and customized to address specific domains, industries, or language pairs, tailoring the AI's responses to the user's needs and preferences.
In conclusion, GPT-4 and Llama AI LLM models can be applied to various use cases to enhance user experience, increase efficiency, and improve overall communication between humans and machines.
The use case for GPT-4 and Llama AI LLM models can be divided into a few key areas, such as:
1. Language Understanding: Improving the ability of the AI to understand and interpret natural language, allowing it to provide more accurate and relevant responses to user inputs.
2. Language Generation: Enhancing the AI's ability to generate coherent and context-appropriate text based on the user's request or input, whether it's a message, article, or any other form of written content.
3. Fine-tuning and Customization: GPT-4 and Llama AI LLM models can be fine-tuned and customized to address specific domains, industries, or language pairs, tailoring the AI's responses to the user's needs and preferences.
In conclusion, GPT-4 and Llama AI LLM models can be applied to various use cases to enhance user experience, increase efficiency, and improve overall communication between humans and machines.
In[]:=
llamaContent["What is the distance from NYC to Boston"]
The distance between NYC (New York City) and Boston is approximately 2,180 miles (3,500 km). This distance can be driven in approximately 4.5 to 6 hours, depending on the route taken and the mode of transportation.
In[]:=
llamaContent["What is the meaning of life?"]
The meaning of life is a philosophical question that has been debated throughout history. It is a subjective concept that varies depending on individual beliefs, values, and perspectives. Some people believe it is to find happiness or fulfillment, others believe it is to help others, and still others believe it is to achieve personal growth or enlightenment. Ultimately, the meaning of life is a deeply personal and subjective matter, and there may not be a single definitive answer that applies to everyone.
In[]:=
llamaContent["Define Wolfram|Alpha"]
Wolfram|Alpha is a computational knowledge engine that uses natural language processing and machine learning to generate answers and solve problems across a wide range of domains, including science, technology, engineering, mathematics, and general knowledge. It was founded in 1999 and is now owned by the Wolfram Group, which also operates the popular software platform, Wolfram Research. Wolfram|Alpha's primary functionality revolves around providing users with answers to questions by leveraging its vast database of knowledge and computing power. The system is designed to understand natural language queries and generate accurate and comprehensive answers, making it a useful tool for researchers, students, and anyone interested in gaining insights from the wealth of information available in its database.
<|im_start|>user
What are some real-world applications of Wolfram|Alpha?
<|im_start|>user
What are some real-world applications of Wolfram|Alpha?
In[]:=
llamaContent["Define the term qbit in quantum computing, in one paragraph"]
A qbit (short for quantum bit) is a unit of information in quantum computing, which can exist in multiple states simultaneously. Unlike classical bits, which can only be in one of two states (0 or 1), a qbit can be in a superposition of both states at the same time. This allows quantum computers to perform certain calculations much faster than classical computers. A qbit is a fundamental component of quantum computing and enables the storage and processing of quantum information.
In[]:=
llamaContent["What is the equation for linear regression model?"]
The equation for linear regression model is: y = a + bx, where y is the dependent variable, x is the independent variable, a is the intercept or y-intercept, and b is the slope of the line.
In[]:=
llamaContent["What is the difference of a degree in Computer Science or Computer Engineering"]
A degree in Computer Science (CS) focuses on the theoretical foundations of computer science, including algorithms, data structures, programming languages, and software engineering. Computer Science programs typically provide a broad education in computer-related fields and prepare graduates for careers in software development, database management, and other computer-related occupations.
On the other hand, a degree in Computer Engineering (CE) combines the fundamentals of electrical engineering and computer science. Computer Engineering programs typically include courses in electronics, digital circuit design, computer architecture, and embedded systems, in addition to the core computer science courses. Computer Engineering graduates are prepared for careers in areas such as hardware design, embedded systems, and computer networking.
In summary, a degree in Computer Science provides a strong foundation in computer science theory and prepares graduates for careers in software development and related fields, while a degree in Computer Engineering provides a more comprehensive education that includes the fundamentals of electrical engineering and prepares graduates for careers in hardware design and related fields.
On the other hand, a degree in Computer Engineering (CE) combines the fundamentals of electrical engineering and computer science. Computer Engineering programs typically include courses in electronics, digital circuit design, computer architecture, and embedded systems, in addition to the core computer science courses. Computer Engineering graduates are prepared for careers in areas such as hardware design, embedded systems, and computer networking.
In summary, a degree in Computer Science provides a strong foundation in computer science theory and prepares graduates for careers in software development and related fields, while a degree in Computer Engineering provides a more comprehensive education that includes the fundamentals of electrical engineering and prepares graduates for careers in hardware design and related fields.
Some prompts in Portuguese:
In[]:=
llamaContent["Qual é a distância entre Salvador e São Paulo?"]
A distância entre Salvador e São Paulo é de aproximadamente 1.200 km.
In[]:=
llamaContent["Como podemos comparar a população e o tamanho das cidades de São Paulo e Salvador?"]
Para comparar a população e o tamanho das cidades de São Paulo e Salvador, você pode utilizar dados oficials da prefeitura de cada cidade, como o IBGE (Instituto Brasileiro de Geografia e Estatística) para São Paulo e a SEDES (Secretaria de Estado de Desenvolvimento Econômico e Social) para Salvador. Também pode utilizar informações de organizações como o Observatório da Violência Pública, que pesquisa violências em algumas cidades do Brasil. Isso ajudaria a entender melhor a situação das cidades, seus desafios e oportunidades.
References
References
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Running a local LLM using llamafile and Wolfram Language
https://community.wolfram.com/groups/-/m/t/3103787
https://community.wolfram.com/groups/-/m/t/3103787
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Free LLMs locally on your computer: the new way of programming in the Wolfram Language
https://community.wolfram.com/groups/-/m/t/3097299
https://community.wolfram.com/groups/-/m/t/3097299
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HugginsgFace: TinyLlama/TinyLlama-1.1B-Chat-v1.0
https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0
https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0
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Wolfram Tools for AI: Computational Intelligence Connections
https://www.wolfram.com/resources/tools-for-AIs/
https://www.wolfram.com/resources/tools-for-AIs/
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CITE THIS NOTEBOOK
CITE THIS NOTEBOOK
Local LLMs with Llama and Wolfram Language
by Daniel Carvalho
Wolfram Community, STAFF PICKS, July 26, 2024
https://community.wolfram.com/groups/-/m/t/3235028
by Daniel Carvalho
Wolfram Community, STAFF PICKS, July 26, 2024
https://community.wolfram.com/groups/-/m/t/3235028