ISBN: 9798248084432 - fine-tuning large language models: adapting gpt and llama architectures for specialized industry tasks (8 risultati)

Paperback. Condizione: New.

Taschenbuch. Condizione: Neu. Neuware - General Intelligence is Good. Specialized Intelligence is Profitable.ChatGPT is a jack of all trades. But can it write compliant medical reports Can it debug your proprietary legacy code The era of 'one-size-fits-all' AI is ending. To build truly valuable applications, you don't just need a Large Language Model-you need a Specialized Language Model. Fine-Tuning Large Language Models is the engineer's guide to taking open-source giants like Llama 3, Mistral, and Falcon and retraining them to master your specific domain.This book moves beyond simple 'Prompt Engineering' or RAG. It teaches you the deep learning techniques required to fundamentally alter a model's behavior, style, and knowledge base-turning a generic chatbot into a precision tool.Your Data. Your Model. Your Rules.Whether you are in finance, law, healthcare, or software, this guide provides the architectural blueprint for domain adaptation.- Parameter-Efficient Fine-Tuning (PEFT): Stop trying to retrain 70 billion parameters. Master LoRA (Low-Rank Adaptation) and QLoRA to fine-tune massive models on a single consumer GPU.- Data Curation Strategies: Learn the most critical skill in AI: formatting your raw documents into high-quality 'Instruction Sets' (JSONL) that actually teach the model.- The 'Teacher-Student' Method: Use stronger models (like GPT-4) to generate synthetic training data to distill knowledge into smaller, faster, cheaper local models.- Evaluation & Benchmarking: How do you know your model is getting better Implement rigorous testing pipelines using BLEU, ROUGE, and LLM-as-a-Judge metrics.- RLHF (Reinforcement Learning from Human Feedback): An introduction to the final step of alignment-teaching your model to prefer helpful, safe, and accurate answers based on human preference data.Whether you are a startup CTO trying to beat the API costs, or a researcher pushing the boundaries of open-source AI, this book hands you the controls.Stop renting intelligence. Build your own. Scroll up and grab your copy to master the art of Fine-Tuning.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. General Intelligence is Good. Specialized Intelligence is Profitable.ChatGPT is a jack of all trades. But can it write compliant medical reports? Can it debug your proprietary legacy code?The era of "one-size-fits-all" AI is ending. To build truly valuable applications, you don't just need a Large Language Model-you need a Specialized Language Model. Fine-Tuning Large Language Models is the engineer's guide to taking open-source giants like Llama 3, Mistral, and Falcon and retraining them to master your specific domain.This book moves beyond simple "Prompt Engineering" or RAG. It teaches you the deep learning techniques required to fundamentally alter a model's behavior, style, and knowledge base-turning a generic chatbot into a precision tool.Your Data. Your Model. Your Rules.Whether you are in finance, law, healthcare, or software, this guide provides the architectural blueprint for domain adaptation.Parameter-Efficient Fine-Tuning (PEFT): Stop trying to retrain 70 billion parameters. Master LoRA (Low-Rank Adaptation) and QLoRA to fine-tune massive models on a single consumer GPU.Data Curation Strategies: Learn the most critical skill in AI: formatting your raw documents into high-quality "Instruction Sets" (JSONL) that actually teach the model.The "Teacher-Student" Method: Use stronger models (like GPT-4) to generate synthetic training data to distill knowledge into smaller, faster, cheaper local models.Evaluation & Benchmarking: How do you know your model is getting better? Implement rigorous testing pipelines using BLEU, ROUGE, and LLM-as-a-Judge metrics.RLHF (Reinforcement Learning from Human Feedback): An introduction to the final step of alignment-teaching your model to prefer helpful, safe, and accurate answers based on human preference data.Whether you are a startup CTO trying to beat the API costs, or a researcher pushing the boundaries of open-source AI, this book hands you the controls.Stop renting intelligence. Build your own. Scroll up and grab your copy to master the art of Fine-Tuning. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: New.

Condizione: New. Print on Demand.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. General Intelligence is Good. Specialized Intelligence is Profitable.ChatGPT is a jack of all trades. But can it write compliant medical reports? Can it debug your proprietary legacy code?The era of "one-size-fits-all" AI is ending. To build truly valuable applications, you don't just need a Large Language Model-you need a Specialized Language Model. Fine-Tuning Large Language Models is the engineer's guide to taking open-source giants like Llama 3, Mistral, and Falcon and retraining them to master your specific domain.This book moves beyond simple "Prompt Engineering" or RAG. It teaches you the deep learning techniques required to fundamentally alter a model's behavior, style, and knowledge base-turning a generic chatbot into a precision tool.Your Data. Your Model. Your Rules.Whether you are in finance, law, healthcare, or software, this guide provides the architectural blueprint for domain adaptation.Parameter-Efficient Fine-Tuning (PEFT): Stop trying to retrain 70 billion parameters. Master LoRA (Low-Rank Adaptation) and QLoRA to fine-tune massive models on a single consumer GPU.Data Curation Strategies: Learn the most critical skill in AI: formatting your raw documents into high-quality "Instruction Sets" (JSONL) that actually teach the model.The "Teacher-Student" Method: Use stronger models (like GPT-4) to generate synthetic training data to distill knowledge into smaller, faster, cheaper local models.Evaluation & Benchmarking: How do you know your model is getting better? Implement rigorous testing pipelines using BLEU, ROUGE, and LLM-as-a-Judge metrics.RLHF (Reinforcement Learning from Human Feedback): An introduction to the final step of alignment-teaching your model to prefer helpful, safe, and accurate answers based on human preference data.Whether you are a startup CTO trying to beat the API costs, or a researcher pushing the boundaries of open-source AI, this book hands you the controls.Stop renting intelligence. Build your own. Scroll up and grab your copy to master the art of Fine-Tuning. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…