Comparison of Popular LLM Models
Here’s a Comparison of Popular LLM Models:
Model/Model Family Name | Created By | Sizes | Versions | Pretraining Data | Fine-tuning and Alignment Details | License | What’s Interesting | Architectural Notes |
---|---|---|---|---|---|---|---|---|
LLaMA 2 | EleutherAI | Not Specified | 2 | Large-scale text corpora | Not specified | MIT License | Advanced language understanding and generation capabilities | Architecture enhancements from LLaMA |
BLOOM | Google Research | Not Specified | Not Specified | Large-scale text corpora | Advanced fine-tuning techniques | Apache License 2.0 | Efficiency and scalability | Cutting-edge algorithms for text summarization |
BERT | Various (e.g., BERT-base, BERT-large) | Multiple | Large-scale text corpora | Extensive fine-tuning options | Apache License 2.0 | Bidirectional context understanding | Bidirectional Encoder Representations from Transformers | |
Falcon 180B | Not specified | 180 billion parameters | Not Specified | Large-scale text corpora | Robust architecture | Not specified | Superior learning capabilities | Massive parameter size |
OPT-175B | Not specified | 175 billion parameters | Not Specified | Large-scale text corpora | State-of-the-art fine-tuning | Not specified | Precision and efficiency | Remarkable fluency and coherence |
XGen-7B | Not specified | 7 billion parameters | Not Specified | Large-scale text corpora | Versatile fine-tuning strategies | Not specified | Versatility and adaptability | Proficiency in diverse NLP tasks |
GPT-NeoX/J | OpenAI | Not Specified | Not Specified | Large-scale text corpora | Community-driven improvements | MIT License | Rivaling proprietary models | Continuous community-driven development |
Vicuna 13-B | Not specified | 13 billion parameters | Not Specified | Large-scale text corpora | Focused fine-tuning strategies | Not specified | Efficiency and accuracy | Customizable parameters and fine-tuning capabilities |
YI 34B | Not specified | 34 billion parameters | Not Specified | Large-scale text corpora | Extensive fine-tuning options | Not specified | Massive parameter size | Superior performance in language-related tasks |
Mixtral 8x7B | Not specified | 8×7 billion parameters | Not Specified | Large-scale text corpora | Innovative training strategies | Not specified | Blend of performance and efficiency | Accessibility and ease of use |
Top 10 Open-Source LLM Models – Large Language Models
Large language models, or LLMs, are essential to the present revolution in generative AI. Language models and interpreters are artificial intelligence (AI) systems that are based on transformers, a potent neural architecture. They are referred to as “large” because they contain hundreds of millions, if not billions, of pre-trained parameters derived from a vast corpus of text data.
In this article, we’ll look at the Top 10 open-source LLMs that will be available in 2024. Even though ChatGPT and (proprietary) LLMs have only been around for a year, the open-source community has made significant progress, and there are now numerous open-source LLMs available for various applications. Read on to discover the most popular!
Top 10 Open-Source LLM Models
- 1. LLaMA 2
- 2. BLOOM
- 3. BERT (Bidirectional Encoder Representations from Transformers)
- 4. Falcon 180B
- 5. OPT-175B
- 6. XGen-7B
- 7. GPT-NeoX and GPT-NeoX
- 8. Vicuna 13-B
- 9. YI 34B
- 10. Mixtral 8x7B
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