LLM Architecture & Training Workshop
Budget: $2 – $8 USD
I’m organizing a deep-dive, live workshop for an audience that already knows its way around machine-learning pipelines and wants to go further—namely, designing and training large language models from scratch.
What I need from you is a hands-on trainer who can walk us through every architectural decision behind a modern LLM, then guide us as we implement and optimize the model in real time. The emphasis is on two areas:
• Model architecture design—layer choices, parameter scaling strategies, tokenizer selection, and the reasoning that ties it all together.
• Model training and optimization—efficient data loading, distributed training tricks, mixed-precision or quantization trade-offs, evaluation loops, and iterative fine-tuning once the base model is up.
Because participants are advanced users, please assume familiarity with PyTorch or TensorFlow, basic NLP concepts, and common tooling such as Hugging Face Transformers, DeepSpeed or FSDP. We’re looking for live interactive sessions—think code-along notebooks, instant Q&A, and step-by-step debugging on shared screens rather than static slides.
By the end of the training, attendees should leave with:
1. A functional, self-trained small-scale LLM they can scale on their own hardware or the cloud.
2. Clear take-home notebooks/scripts illustrating each architectural decision and performance tweak discussed.
3. A concise reference sheet of best practices for future experiments.
If you can deliver this level of practical, real-time instruction, let’s set dates and technical prerequisites and get building.
What I need from you is a hands-on trainer who can walk us through every architectural decision behind a modern LLM, then guide us as we implement and optimize the model in real time. The emphasis is on two areas:
• Model architecture design—layer choices, parameter scaling strategies, tokenizer selection, and the reasoning that ties it all together.
• Model training and optimization—efficient data loading, distributed training tricks, mixed-precision or quantization trade-offs, evaluation loops, and iterative fine-tuning once the base model is up.
Because participants are advanced users, please assume familiarity with PyTorch or TensorFlow, basic NLP concepts, and common tooling such as Hugging Face Transformers, DeepSpeed or FSDP. We’re looking for live interactive sessions—think code-along notebooks, instant Q&A, and step-by-step debugging on shared screens rather than static slides.
By the end of the training, attendees should leave with:
1. A functional, self-trained small-scale LLM they can scale on their own hardware or the cloud.
2. Clear take-home notebooks/scripts illustrating each architectural decision and performance tweak discussed.
3. A concise reference sheet of best practices for future experiments.
If you can deliver this level of practical, real-time instruction, let’s set dates and technical prerequisites and get building.