Large Language Models (LLMs) have become pivotal in advancing Natural Language Processing in the rapidly evolving world of Artificial Intelligence, extending beyond just ChatGPT.
Hugging Face 🤗, a leading AI community and platform, hosts many of these models, offering incredible versatility and power. This blog post delves into the top 10 LLMs on Hugging Face as of the beginning of 2024, comparing their technical details and capabilities. Notably, these models can be directly used online within Notebooks or downloaded for local usage, broadening their accessibility.
But let’s cut to the chase, here is the list!
The 🤗Top 10
microsoft/phi-2: As the best-pretrained model around 3B parameters, this model sets a high standard in text generation and natural language understanding. Its moderate size makes it efficient for a variety of applications. Given the nature of the training data, the Phi-2 model is best suited for prompts using the QA format, the chat format, and the code format.
rishiraj/CatPPT-base: With 7B parameters, this model strikes a balance between computational demand and language proficiency, excelling in more complex language tasks. “The purrfect alternative to that other big cat in town, known for keeping all the secrets to itself! Our feline friend here is created through merging openchat and neuralchat models using Gradient SLERP method and then finetuned on no_robots dataset for chat“
cloudyu/Mixtral_7Bx2_MoE: Standing out with around 13B parameters, this model demonstrates enhanced capability in handling intricate language models, making it suitable for advanced text generation and analysis. It is particularly well-suited for both general-purpose applications and more complex challenges, offering a balanced approach that leverages the strengths of its constituent models.
chargoddard/Yi-34B-Llama: This 30B parameter model is renowned for its depth in processing and generating large texts, offering significant improvements in accuracy and context understanding. With tensors renamed to match standard Llama modelling code. Model can be loaded without trust_remote_code, but the tokenizer can not.
deepseek-ai/deepseek-llm-67b-base: A giant in the field with approximately 65B parameters, it’s tailored for extremely complex language tasks, setting benchmarks in AI-driven text generation and comprehension. It outperforms Llama2 70B Base in areas such as reasoning, coding, math, and Chinese comprehension. It also demonstrates remarkable generalization abilities, as evidenced by its exceptional score of 65 on the Hungarian National High School Exam.
Despite the models so far being extremely powerful, they also require substantial hardware to run smoothly. But not everyone owns a data center… So let’s dive into the fine-tuned mini LLMs, capable of running on smaller and less powerful hardware.
Walmart-the-bag/Solar-10.7B-Cato: As the best fine-tuned model around 1B parameters, it excels in specialized tasks, demonstrating the power of fine-tuning in AI. It is a finetuned version of Sakura-SOLAR-Instruct on GPT4 data, and it is meant to be used for questioning and instruction, finetuned for questions.
GeneZC/miniChat-2-3B: Another fine-tuned model with around 3B parameters, known for its efficiency and effectiveness in specific language tasks. A language model continued from MiniMA-3B and finetuned on both instruction and preference data. Surpassing Vicuna-7B and approximating LLaMA-2-Chat-7B on MT-Bench.
quantumaikr/quantum-dpo-v0.1: This model, with around 7B parameters, shows the advancements in fine-tuning, providing a nuanced approach to language modeling. It is intended for research only, in adherence with the CC BY-NC-4.0 license
kyujinpy/Sakura-SOLAR-Instruct: With 13B parameters, it illustrates how fine-tuning can significantly enhance a model’s capability in understanding and generating complex languages. It received the global LLM Rank 1 in December 2023.
And finally.. the winner!
fblgit/una-xaberius-34b-v1beta: A top-tier fine-tuned model with around 30B parameters, demonstrating exceptional language processing abilities, especially in creative and complex text generation. It is an experimental 34B LLaMa-Yi-34 B-based model, best in its series, trained on SFT, DPO, and UNA (Unified Neural Alignment) on multiple datasets.
So, How Do Those Compare to GPT?
In comparison, OpenAI‘s GPT-4, a behemoth with over 175 billion parameters, stands as a testament to the cutting-edge developments in the field. This model, known for its deep learning and vast training data, offers insights into the future direction of AI-driven language processing, setting a high bar for LLMs regarding versatility, depth, and accuracy.
But not every organization is a competitor of OpenAI! In most cases, Open Source LLMs are a great alternative to the famous ChatGPT models.
A Real Fast Pace
As we witness these groundbreaking advancements, it’s clear that the LLM landscape is constantly and rapidly changing. New models emerge regularly, each outdoing the last in terms of sophistication and capabilities. These advancements aren’t just in model size; they encompass new training technologies and methodologies that continually push the boundaries of what’s possible in AI.
Given this ever-evolving scenario, staying up-to-date with the latest developments can be daunting. This is where the role of an AI consultant becomes invaluable. An AI consultant not only keeps track of these rapid changes but also guides leveraging the right models for specific needs. They ensure that businesses and individuals harness the full potential of these powerful tools, making informed decisions in a landscape that’s as exciting as it is unpredictable.
In conclusion, as we navigate through this golden era of AI, understanding and utilizing the capabilities of these LLMs becomes crucial. Whether it’s for business, research, or personal development, the power of language models like those on Hugging Face and OpenAI’s GPT-4 is reshaping our approach to AI and its applications in our daily lives.
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