What are the LLaMA, LLaMA 2, and BLOOM language models?

The LLaMA, LLaMA 2, and BLOOM language models are:

1. Large Language Models

• These large language models are developed by the community, including Meta and Hugging Face. Meta releases the LLaMA models, including LLaMA 2, ranging from 7 to 65 billion parameters.
• Hugging Face receives funding from the French government to train the BLOOM model with 176 billion parameters. These models are open to the community but still require expensive hardware for acceleration. While the performance of these open models is not yet on par with those from large companies, they have gained recognition within the community.

2. Related Theories:

Meta’s LLaMA models:

• Parameter count: ranging from 7 to 65 billion.
• Pros: Open for community research, potential for non-commercial research and applications.
• Cons: Requires expensive hardware for model execution, performance still lower than models from large companies like OpenAI.

BLOOM model:

• Parameter count: 176 billion.
• Pros: Open for the community, easily accessible for research and experimentation.
• Cons: Still requires hardware acceleration for model execution, performance not yet as expected.

LLaMA 2 models:

• • Parameter count: Not specified, but trained on 2 trillion tokens.
  • Pros: High performance, comparable to closed models, increased reliability.
  • Cons: Not entirely open, Meta does not release training data, and requires permission from large companies like Google before use.

• LLaMA models:

  • Deep dive into Meta’s LLaMA model and large language models

• Language model development:

  • Exploring the deep power and challenges of large language models

• BLOOM model:

  • Progress of OPT and BLOOM projects in language research

• Model parameters:

  • Language model performance and optimization of size and training data

• Language model performance:

  • Comparison between GPT-3 and GLAM: Performance and potential in AI

• Hardware acceleration:

  • The crucial role of supercomputing infrastructure in developing large-scale NLP models

• Open for research:

  • Unveiling the landscape of artificial intelligence: Opportunities and challenges in business

• Community-driven research:

  • Progress of PALM, OPT, and BLOOM in language models