Exploration of Language Learning Models (LLMs)

For advanced Language Learning Models, consider Flan-UL2. This model requires significant VRAM but provides excellent results with <2s inference speed. It's great for zero-shot tasks and prevents hallucinations.

Proper formatting and instruction tuning are key to maximizing your model's performance. You may find useful information on system, user, and special character formatting for messages on promptingguide.ai. Tools like Langchain or Transformer Agents can help abstract this process.

Be aware of the issue of cherry-picking in machine learning research. It's recommended to scrutinize the data and methods used in research papers. OpenAI's approach to evaluation and refinement, which is based on high-quality metrics and a mature QA process, is a good example to follow.

While there's a lot of hype around LLMs, some of them like Llama and Bard have potential. Also, rigorous benchmarking of performance as seen with GPT-4 is a positive sign.

Try the chatbot arena as a benchmark to evaluate the quality of different models.

Lastly, take into consideration the limitations of open-source LLMs and the benefits of instruction-tuned models. Models like Alpaca-x-GPT-4 13B are among the promising ones for local use.

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