LLM Text Generation R&D Mentor - 06/07/2026 02:19 EDT
Budget: $2 – $8 USD
I’m at the very start of my AI journey and I want to dive straight into research-oriented work on text generation with large language models. My objective is to understand today’s leading architectures—think GPT-style transformers—and then reproduce, extend, and evaluate recent research in natural language processing.
Here’s what I need from you:
• A clear learning roadmap that walks me through the essential papers, repos, and concepts behind modern text-generation systems.
• Hands-on guidance while I set up a Python environment (PyTorch or TensorFlow, Hugging Face Transformers, perhaps LangChain) and fine-tune or train a model on a small dataset.
• Practical notebooks that generate text, document the hyper-parameters used, and demonstrate evaluation techniques such as perplexity, BLEU, and human-readability checks.
• Regular feedback sessions—screen-sharing, code reviews, troubleshooting—to make sure I truly understand what’s happening under the hood, from tokenisation to sampling strategies.
• A short wrap-up report summarising the experiments we ran, the results we achieved, and next steps for deeper research.
Acceptance criteria
– All notebooks must execute end-to-end on my local machine or a free Colab instance without modification.
– I should be able to explain each major block of code back to you during our final session and reproduce the same generation outputs.
I’d like to start next week and can commit to one or two live sessions per week for about a month, with flexibility if we need more time. If you have published papers, open-sourced projects, or Kaggle work related to text generation, please include a link so I can get a feel for your style.
Let’s push some boundaries together and get me confidently building with LLMs.
Here’s what I need from you:
• A clear learning roadmap that walks me through the essential papers, repos, and concepts behind modern text-generation systems.
• Hands-on guidance while I set up a Python environment (PyTorch or TensorFlow, Hugging Face Transformers, perhaps LangChain) and fine-tune or train a model on a small dataset.
• Practical notebooks that generate text, document the hyper-parameters used, and demonstrate evaluation techniques such as perplexity, BLEU, and human-readability checks.
• Regular feedback sessions—screen-sharing, code reviews, troubleshooting—to make sure I truly understand what’s happening under the hood, from tokenisation to sampling strategies.
• A short wrap-up report summarising the experiments we ran, the results we achieved, and next steps for deeper research.
Acceptance criteria
– All notebooks must execute end-to-end on my local machine or a free Colab instance without modification.
– I should be able to explain each major block of code back to you during our final session and reproduce the same generation outputs.
I’d like to start next week and can commit to one or two live sessions per week for about a month, with flexibility if we need more time. If you have published papers, open-sourced projects, or Kaggle work related to text generation, please include a link so I can get a feel for your style.
Let’s push some boundaries together and get me confidently building with LLMs.