AIcademy
Fine-tuning and Embedding optimization for LLMs
Introduction
In this in-depth training you will learn how to adapt large language models (LLMs) to your organizational context, writing style and content domain. You will work with techniques such as fine-tuning , LoRA (Low-Rank Adaptation), embedding filtering and prompt-tuning to make existing foundation models perform more accurately on your own data.
We will cover both hosted solutions (such as OpenAI's fine-tuning API) and open-source workflows via Hugging Face Transformers , with a focus on performance, scalability, cost, and security. After this training, you will not only be able to make language models fit your content better, but also be able to judge when to fine-tune and when a retrieval-based approach with embeddings is sufficient.
Learning objectives
Understanding the difference between fine-tuning, prompt-tuning, and contextual embeddings (RAG)
Working with OpenAI's fine-tuning API (GPT-3.5/4) and Hugging Face Trainer pipelines
Training models with techniques such as full fine-tuning, parameter-efficient tuning (LoRA, QLoRA)
Prepare domain-specific datasets and correctly structure model labels
Apply embedding filtering to improve the quality of your context retrieval (RAG optimization)
Evaluation and Validation Strategies for Adapted LLMs
Best practices for safe, scalable, and maintainable model modifications
Approach and working methods
This training is completely hands-on and focuses on actually adapting models to your own data or context. You will work on an end-to-end pipeline: from data preparation and model selection to training, evaluation and integration.
There is room to choose between different tool stacks depending on your technical environment, including:
OpenAI API (via CLI or Python SDK)
Hugging Face Transformers & Datasets
QLoRA with PEFT and bitsandbytes
Vector databases such as FAISS, Weaviate or Pinecone (for context retrieval and embedding optimization)
If desired, we can tailor the training to your own use case or existing infrastructure.
For whom
This course is intended for AI engineers, ML specialists and data scientists who want to tailor existing LLMs to their domain, workflows or customer context. Some experience with Python and knowledge of LLM architectures (such as GPT, LLaMA, Mistral) is recommended.
Interested in this training?
Feel free to contact us. We are happy to think along about a suitable solution for your team, project or organization.
Fine-tuning and Embedding optimization for LLMs
Introduction
In this in-depth training you will learn how to adapt large language models (LLMs) to your organizational context, writing style and content domain. You will work with techniques such as fine-tuning , LoRA (Low-Rank Adaptation), embedding filtering and prompt-tuning to make existing foundation models perform more accurately on your own data.
We will cover both hosted solutions (such as OpenAI's fine-tuning API) and open-source workflows via Hugging Face Transformers , with a focus on performance, scalability, cost, and security. After this training, you will not only be able to make language models fit your content better, but also be able to judge when to fine-tune and when a retrieval-based approach with embeddings is sufficient.
Learning objectives
Understanding the difference between fine-tuning, prompt-tuning, and contextual embeddings (RAG)
Working with OpenAI's fine-tuning API (GPT-3.5/4) and Hugging Face Trainer pipelines
Training models with techniques such as full fine-tuning, parameter-efficient tuning (LoRA, QLoRA)
Prepare domain-specific datasets and correctly structure model labels
Apply embedding filtering to improve the quality of your context retrieval (RAG optimization)
Evaluation and Validation Strategies for Adapted LLMs
Best practices for safe, scalable, and maintainable model modifications
Approach and working methods
This training is completely hands-on and focuses on actually adapting models to your own data or context. You will work on an end-to-end pipeline: from data preparation and model selection to training, evaluation and integration.
There is room to choose between different tool stacks depending on your technical environment, including:
OpenAI API (via CLI or Python SDK)
Hugging Face Transformers & Datasets
QLoRA with PEFT and bitsandbytes
Vector databases such as FAISS, Weaviate or Pinecone (for context retrieval and embedding optimization)
If desired, we can tailor the training to your own use case or existing infrastructure.
For whom
This course is intended for AI engineers, ML specialists and data scientists who want to tailor existing LLMs to their domain, workflows or customer context. Some experience with Python and knowledge of LLM architectures (such as GPT, LLaMA, Mistral) is recommended.
Interested in this training?
Feel free to contact us. We are happy to think along about a suitable solution for your team, project or organization.
Description:
Adapt existing language models (LLMs) to your own data, tone of voice or organizational context using techniques such as fine-tuning, LoRA and embedding filtering.
Learning objectives:
Difference between fine-tuning, prompt-tuning and context-embedding.
Working with OpenAI fine-tuning API and Hugging Face Transformers.
Training and evaluating domain-specific models.
Best practices for safe, scalable model modifications.
For whom: Engineers, developers, data scientists and data specialists who want to adapt language models to their context.
Fine-tuning and Embedding optimization for LLMs
Introduction
In this in-depth training you will learn how to adapt large language models (LLMs) to your organizational context, writing style and content domain. You will work with techniques such as fine-tuning , LoRA (Low-Rank Adaptation), embedding filtering and prompt-tuning to make existing foundation models perform more accurately on your own data.
We will cover both hosted solutions (such as OpenAI's fine-tuning API) and open-source workflows via Hugging Face Transformers , with a focus on performance, scalability, cost, and security. After this training, you will not only be able to make language models fit your content better, but also be able to judge when to fine-tune and when a retrieval-based approach with embeddings is sufficient.
Learning objectives
Understanding the difference between fine-tuning, prompt-tuning, and contextual embeddings (RAG)
Working with OpenAI's fine-tuning API (GPT-3.5/4) and Hugging Face Trainer pipelines
Training models with techniques such as full fine-tuning, parameter-efficient tuning (LoRA, QLoRA)
Prepare domain-specific datasets and correctly structure model labels
Apply embedding filtering to improve the quality of your context retrieval (RAG optimization)
Evaluation and Validation Strategies for Adapted LLMs
Best practices for safe, scalable, and maintainable model modifications
Approach and working methods
This training is completely hands-on and focuses on actually adapting models to your own data or context. You will work on an end-to-end pipeline: from data preparation and model selection to training, evaluation and integration.
There is room to choose between different tool stacks depending on your technical environment, including:
OpenAI API (via CLI or Python SDK)
Hugging Face Transformers & Datasets
QLoRA with PEFT and bitsandbytes
Vector databases such as FAISS, Weaviate or Pinecone (for context retrieval and embedding optimization)
If desired, we can tailor the training to your own use case or existing infrastructure.
For whom
This course is intended for AI engineers, ML specialists and data scientists who want to tailor existing LLMs to their domain, workflows or customer context. Some experience with Python and knowledge of LLM architectures (such as GPT, LLaMA, Mistral) is recommended.
Interested in this training?
Feel free to contact us. We are happy to think along about a suitable solution for your team, project or organization.
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