Alpaca - Revision history

BPeat at 02:09, 27 April 2024

2024-04-27T02:09:55Z

BPeat at 13:47, 23 March 2024

2024-03-23T13:47:08Z

BPeat: Text replacement - "* Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu" to "* Conversational AI ... [[C...

2024-03-16T15:48:26Z

BPeat at 01:44, 3 September 2023

2023-09-03T01:44:44Z

BPeat at 01:00, 17 August 2023

2023-08-17T01:00:24Z

BPeat at 00:56, 17 August 2023

2023-08-17T00:56:43Z

BPeat at 22:26, 12 July 2023

2023-07-12T22:26:44Z

BPeat at 01:52, 10 July 2023

2023-07-10T01:52:32Z

BPeat at 00:45, 6 July 2023

2023-07-06T00:45:37Z

BPeat at 11:38, 5 July 2023

2023-07-05T11:38:45Z

@@ Line 29: / Line 29: @@
 * [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
-* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
+* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]

@@ Line 27: / Line 27: @@
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
-* [[Assistants]] ... [[Personal Companions]] ... [[Agents]]  ... [[Negotiation]] ... [[LangChain]]
+* [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
 * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]

@@ Line 26: / Line 26: @@
 * [[Large_Language_Model_(LLM)#Large Language Model (LLM) Ecosystem Explained|Large Language Model (LLM) Ecosystem Explained]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
-* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
+* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
 * [[Assistants]] ... [[Personal Companions]] ... [[Agents]]  ... [[Negotiation]] ... [[LangChain]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]

@@ Line 25: / Line 25: @@
 * [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
 * [[Large_Language_Model_(LLM)#Large Language Model (LLM) Ecosystem Explained|Large Language Model (LLM) Ecosystem Explained]]
-* [[Generative AI]] ... [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
+* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [[Assistants]] ... [[Personal Companions]] ... [[Agents]]  ... [[Negotiation]] ... [[LangChain]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]

@@ Line 35: / Line 35: @@
 Not only does this model run on modest hardware, but it can even be retrained on a modest budget to fine-tune it for new use cases. Using their methods, the team showed it was possible to retrain their LLM for less than $600. [https://www.hackster.io/news/alpaca-the-large-language-model-that-won-t-fleece-you-cd133fec7412 Alpaca: The Large Language Model That Won't Fleece You | Nick Bild - Hackster.io] ... Alpaca builds on LLaMA to make large language models more accessible, demonstrating that they can be retrained for new uses for under $600.
-Fine-tuning is a process of adjusting the parameters of a pre-trained model to improve its performance on a specific task. In the case of Alpaca, fine-tuning can be used to improve its performance on a variety of tasks, such as:
+[[Fine-tuning]] is a process of adjusting the parameters of a pre-trained model to improve its performance on a specific task. In the case of Alpaca, [[fine-tuning]] can be used to improve its performance on a variety of tasks, such as:
 * Generating text in a specific style or genre
@@ Line 44: / Line 44: @@
 To fine-tune Alpaca, you will need a dataset of text and code that is relevant to the task you want to improve. The dataset should be large enough to provide the model with enough data to learn from.
-Once you have a dataset, you can use a fine-tuning framework, such as Hugging Face Transformers, to fine-tune the Alpaca model. The fine-tuning process typically involves adjusting the model's parameters using a technique called backpropagation.
+Once you have a dataset, you can use a [[fine-tuning]] framework, such as Hugging Face Transformers, to fine-tune the Alpaca model. The [[fine-tuning]] process typically involves adjusting the model's parameters using a technique called backpropagation.
-The amount of time it takes to fine-tune Alpaca depends on the size of the dataset and the complexity of the task. In general, fine-tuning Alpaca can take several hours to several days.
+The amount of time it takes to fine-tune Alpaca depends on the size of the dataset and the complexity of the task. In general, [[fine-tuning]] Alpaca can take several hours to several days.
-The results of fine-tuning Alpaca can vary depending on the dataset and the task. In some cases, fine-tuning can significantly improve the model's performance. In other cases, the improvement may be more modest.
+The results of [[fine-tuning]] Alpaca can vary depending on the dataset and the task. In some cases, [[fine-tuning]] can significantly improve the model's performance. In other cases, the improvement may be more modest.
-Overall, fine-tuning is a powerful technique that can be used to improve the performance of Alpaca on a variety of tasks. If you are interested in fine-tuning Alpaca, there are a number of resources available online, including tutorials and pre-trained models.
+Overall, [[fine-tuning]] is a powerful technique that can be used to improve the performance of Alpaca on a variety of tasks. If you are interested in [[fine-tuning]] Alpaca, there are a number of resources available online, including tutorials and pre-trained models.
 Here are some additional resources that you may find helpful:

← Older revision		Revision as of 00:56, 17 August 2023
Line 34:		Line 34:

	Not only does this model run on modest hardware, but it can even be retrained on a modest budget to fine-tune it for new use cases. Using their methods, the team showed it was possible to retrain their LLM for less than $600. [https://www.hackster.io/news/alpaca-the-large-language-model-that-won-t-fleece-you-cd133fec7412 Alpaca: The Large Language Model That Won't Fleece You \| Nick Bild - Hackster.io] ... Alpaca builds on LLaMA to make large language models more accessible, demonstrating that they can be retrained for new uses for under $600.		Not only does this model run on modest hardware, but it can even be retrained on a modest budget to fine-tune it for new use cases. Using their methods, the team showed it was possible to retrain their LLM for less than $600. [https://www.hackster.io/news/alpaca-the-large-language-model-that-won-t-fleece-you-cd133fec7412 Alpaca: The Large Language Model That Won't Fleece You \| Nick Bild - Hackster.io] ... Alpaca builds on LLaMA to make large language models more accessible, demonstrating that they can be retrained for new uses for under $600.
		+
		+	Fine-tuning is a process of adjusting the parameters of a pre-trained model to improve its performance on a specific task. In the case of Alpaca, fine-tuning can be used to improve its performance on a variety of tasks, such as:
		+
		+	* Generating text in a specific style or genre
		+	* Translating languages more accurately
		+	* Answering questions more comprehensively
		+	* Writing different kinds of creative content
		+
		+	To fine-tune Alpaca, you will need a dataset of text and code that is relevant to the task you want to improve. The dataset should be large enough to provide the model with enough data to learn from.
		+
		+	Once you have a dataset, you can use a fine-tuning framework, such as Hugging Face Transformers, to fine-tune the Alpaca model. The fine-tuning process typically involves adjusting the model's parameters using a technique called backpropagation.
		+
		+	The amount of time it takes to fine-tune Alpaca depends on the size of the dataset and the complexity of the task. In general, fine-tuning Alpaca can take several hours to several days.
		+
		+	The results of fine-tuning Alpaca can vary depending on the dataset and the task. In some cases, fine-tuning can significantly improve the model's performance. In other cases, the improvement may be more modest.
		+
		+	Overall, fine-tuning is a powerful technique that can be used to improve the performance of Alpaca on a variety of tasks. If you are interested in fine-tuning Alpaca, there are a number of resources available online, including tutorials and pre-trained models.
		+
		+	Here are some additional resources that you may find helpful:
		+
		+	* [https://replicate.com/blog/fine-tune-alpaca-with-lora Fine-tuning Alpaca with LoRA]
		+	* Fine-tuning Alpaca and LLaMA: [https://www.mlexpert.io/machine-learning/tutorials/alpaca-fine-tuning Training on a Custom Dataset:]
		+	* Fine-Tuning Llama-2: [https://www.anyscale.com/blog/fine-tuning-llama-2-a-comprehensive-case-study-for-tailoring-models-to-unique-applications A Comprehensive Case Study:]
		+

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=ai+Alpaca+Standford YouTube]
@@ Line 18: / Line 27: @@
 * [[Generative AI]]  ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
 * [[Assistants]] ... [[Personal Companions]] ... [[Agents]]  ... [[Negotiation]] ... [[LangChain]]
-* [[Development]]  ...[[Development#AI Pair Programming Tools|AI Pair Programming Tools]] ... [[Analytics]]  ... [[Visualization]]  ... [[Diagrams for Business Analysis]]
+* [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Loop]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Bayes]] ... [[Network Pattern]]