Introduction

Hugging Face has become a treasure trove for natural language processing enthusiasts and developers, offering a diverse collection of pre-trained language models that can be easily integrated into various applications. In the world of Large Language Models (LLMs), Hugging Face stands out as a go-to platform. This article explores the top 10 LLM models available on Hugging Face, each contributing to the evolving landscape of language understanding and generation.

Let’s begin! 

Mistral-7B-v0.1

The Mistral-7B-v0.1 is a Large Language Model (LLM) boasting a substantial 7 billion parameters. It is designed as a pretrained generative text model and is notable for surpassing benchmarks set by Llama 2 13B across various tested domains. The model is based on a transformer architecture with specific choices in attention mechanisms, such as Grouped-Query Attention and Sliding-Window Attention. The Mistral-7B-v0.1 also incorporates a Byte-fallback BPE tokenizer.

Use Cases and Applications

• Text Generation: The Mistral-7B-v0.1 is well-suited for applications requiring high-quality text generation, such as content creation, creative writing, or automated storytelling.
• Natural Language Understanding: With its advanced transformer architecture and attention mechanisms, the model can be applied to tasks involving natural language understanding, including sentiment analysis and text classification.
• Language Translation: Given its generative capabilities and large parameter size, the model may excel in language translation tasks, where nuanced and contextually accurate translations are crucial.
• Research and Development: Researchers and developers can leverage Mistral-7B-v0.1 as a base model for further experimentation and fine-tuning in a wide range of natural language processing projects.

You can access this LLM here.

Starling-LM-11B-alpha

This large language model (LLM) has 11 billion parameters, emerged from NurtureAI. It utilizes the OpenChat 3.5 model as its foundation and undergoes fine-tuning through Reinforcement Learning from AI Feedback (RLAIF), a novel reward training and policy tuning pipeline. This approach relies on a dataset of human-labeled rankings to direct the training process.

Use Cases and Applications

Starling-LM-11B-alpha is a promising large language model with the potential to revolutionize the way we interact with machines. Its open-source nature, strong performance, and diverse capabilities make it a valuable tool for researchers, developers, and creative professionals alike.

• Natural language processing (NLP) applications: Generating realistic dialogue for chatbots and virtual assistants, writing creative text formats, translating languages, and summarizing text.
• Machine learning research: Contributing to the development of new NLP algorithms and techniques.
• Education and training: Providing personalized learning experiences and generating interactive content.
• Creative industries: Generating scripts, poems, song lyrics, and other creative content.

Click here to explore this hugging face model.

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Yi-34B-Llama

Boasting 34 billion parameters, Yi-34B-Llama demonstrates enhanced learning capacity compared to smaller models. It excels in multi-modal capabilities, efficiently processing text, code, and images for versatility beyond single-modality models. Embracing zero-shot learning, Yi-34B-Llama adapts to tasks it hasn’t explicitly trained on, showcasing its flexibility in new scenarios. Additionally, its stateful nature enables it to remember past conversations and interactions, contributing to a more engaging and personalized user experience.

Use Cases of Yi-34B-Llama

• Text generation: Yi-34B-Llama can be used to generate different creative text formats, like poems, code, scripts, musical pieces, email, letters, etc.
• Machine translation: Yi-34B-Llama can translate languages accurately and fluently.
• Question answering: Yi-34B-Llama can answer your questions in an informative way, even if they are open ended, challenging, or strange.
• Dialogue: Yi-34B-Llama can hold engaging and informative conversations on a wide range of topics.
• Code generation: Yi-34B-Llama can generate code for a variety of programming languages.
• Image captioning: Yi-34B-Llama can accurately describe the content of an image.

You can access this LLM here.

DeepSeek LLM 67B Base

DeepSeek LLM 67B Base, a 67-billion parameter large language model (LLM) has garnered attention for its exceptional performance in reasoning, coding, and mathematics. Outshining counterparts like Llama2 70B Base, the model achieves a HumanEval Pass@1 score of 73.78, excelling in code understanding and generation. Its remarkable math skills are evident in scores on benchmarks such as GSM8K 0-shot (84.1) and Math 0-shot (32.6). Additionally, surpassing GPT-3.5 in Chinese language capabilities, DeepSeek LLM 67B Base is open source under the MIT license, enabling free exploration and experimentation by researchers and developers.

Use Cases and Application

• Programming: Utilize DeepSeek LLM 67B Base for tasks such as code generation, code completion, and bug fixing.
• Education: Leverage the model to develop intelligent tutoring systems and personalized learning tools.
• Research: Employ DeepSeek LLM 67B Base to explore various areas of natural language processing research.
• Content Creation: Harness the model’s capabilities to generate creative text formats like poems, scripts, musical pieces, and more.
• Translation: Rely on DeepSeek LLM 67B Base for highly accurate language translation.
• Question Answering: The model comprehensively and informatively addresses questions, even if they are open-ended, challenging, or unusual.

You can access this LLM here.

MiniChat-1.5-3B

MiniChat-1.5-3B, a language model adapted from LLaMA2-7B, excels in conversational AI tasks. Competitive with larger models, it offers high performance, surpassing 3B competitors in GPT4 evaluation and rivals 7B chat models. Distilled for data efficiency, it maintains a smaller size and faster inference speed. Applying NEFTune and DPO techniques ensures improved dialogue fluency. Trained on a vast dataset of text and code, it possesses a broad knowledge base. MiniChat-1.5-3B is multi-modal, accommodating text, images, and audio for diverse and dynamic interactions across various applications.

Use Cases and Application

• Chatbots and Virtual Assistants: Develop engaging and informative chatbots for customer service, education, and entertainment.
• Dialog Systems: Create chat interfaces for applications like social media platforms, games, and smart home devices.
• Storytelling and Creative Writing: Generate compelling stories, scripts, poems, and other creative text formats.
• Question Answering and Information Retrieval: Answer user queries accurately and efficiently, providing relevant information in a conversational style.
• Code Generation and Translation: Generate code snippets and translate between programming languages.
• Interactive Learning and Education: Develop personalized and interactive learning experiences for students of all ages.

You can access this large language model here.

Marcoroni-7B-v3

Marcoroni-7B-v3, a 7-billion parameter multilingual generative model, exhibits diverse capabilities encompassing text generation, language translation, creative content creation, and informative question answering. With a focus on efficiency and versatility, Marcoroni-7B-v3 processes both text and code, making it a dynamic tool for various tasks. Boasting 7 billion parameters, it excels in learning complex language patterns, yielding realistic and nuanced outputs. Leveraging zero-shot learning, the model adeptly performs tasks without prior training or fine-tuning, ideal for rapid prototyping and experimentation. Marcoroni-7B-v3 further democratizes access, being open source and available under a permissive license, facilitating widespread utilization and experimentation by users worldwide.

Use Cases and Application

• Text Generation: Marcoroni-7B-v3 can be used to generate realistic and creative text formats, including poems, code, scripts, musical pieces, emails, and letters.
• Machine Translation: Marcoroni-7B-v3 excels in translating between languages with high accuracy and fluency.
• Chatbots: Create engaging chatbots with natural conversational abilities using Marcoroni-7B-v3.
• Code Generation: Utilize Marcoroni-7B-v3 to generate code from natural language descriptions.
• Question Answering: Marcoroni-7B-v3 comprehensively answers questions, even if they are open-ended, challenging, or unusual.
• Summarization: Employ Marcoroni-7B-v3 for summarizing lengthy texts into shorter and more concise summaries.
• Paraphrasing: Marcoroni-7B-v3 effectively paraphrases text while preserving its original meaning.
• Sentiment Analysis: Utilize Marcoroni-7B-v3 for analyzing the sentiment of text.

You can access this hugging face model here!

Nyxene-v2-11B

Developed by Hugging Face, Nyxene-v2-11B stands as a formidable large language model (LLM), armed with an impressive 11 billion parameters. This extensive parameter size equips Nyxene-v2-11B to adeptly handle intricate and diverse tasks. It excels in processing information and generating text with heightened accuracy and fluency compared to smaller models. Furthermore, Nyxene-v2-11B is available in the efficient BF16 format, ensuring faster inference and reduced memory usage for optimized performance. Notably, it eliminates the need for an additional 1% tokens, simplifying usage compared to its predecessor without compromising performance.

Use Cases and Application

• Text Generation: Utilize Nyxene-v2-11B to create various creative text formats such as poems, scripts, musical pieces, emails, letters, and more.
• Question Answering: The model comprehensively and informatively addresses your questions, even if they are open-ended, challenging, or unusual.
• Code Completion: Leverage Nyxene-v2-11B for efficient code completion, aiding developers in writing code faster and more effectively.
• Translation: Accurately and fluently translate between languages using the capabilities of the model.
• Data Summarization: Nyxene-v2-11B excels in summarizing large amounts of text into concise and informative summaries, saving time and effort.
• Chatbots: Employ the model to craft engaging and informative chatbots capable of answering questions and providing assistance.

You can access this LLM here!

Una Xaberius 34B v1Beta

This is an experimental large language model (LLM) based on the LLaMa-Yi-34B architecture, was created by FBL and released in December 2023. Boasting 34 billion parameters, it places among the larger LLMs, promising robust performance and versatility.

Trained on multiple datasets using innovative techniques like SFT, DPO, and UNA (Unified Neural Alignment), this model has secured the top spot on the Hugging Face LeaderBoard in OpenSource LLMs, achieving impressive scores in various evaluations.

Una Xaberius 34B v1Beta excels in understanding and responding to diverse prompts, particularly those in ChatML and Alpaca System format. Its capabilities span answering questions, generating creative text formats, and executing tasks like poetry, code generation, email writing, and more. In the evolving landscape of large language models, Una Xaberius 34B v1Beta emerges as a robust contender, pushing the boundaries of language understanding and generation.

Use Cases and Application

• Chatbots and virtual assistants: Una Xaberius’s ability to hold engaging conversations makes it ideal for chatbot and virtual assistant applications.
• Content creation: From writing stories and poems to generating scripts and musical pieces, Una Xaberius can be a valuable tool for creators.
• Code generation and analysis: With its understanding of code, Una Xaberius can assist programmers in generating code snippets and analyzing existing code.
• Education and training: Una Xaberius can be used to create personalized learning experiences and provide interactive training materials.
• Research and development: As a powerful language model, Una Xaberius can be used for research in natural language processing, artificial intelligence, and other related fields.

You can access this hugging face model here!

ShiningValiant

Valiant Labs introduces ShiningValiant, a large language model (LLM) built on the Llama 2 architecture and meticulously finetuned on various datasets to embody insights, creativity, passion, and friendliness.

With a substantial 70 billion parameters, ShiningValiant ranks among the largest LLMs available, enabling it to generate text that is not only comprehensive but also nuanced, surpassing the capabilities of smaller models.

Incorporating innovative safeguards, it employs safetensors, a safety filter designed to prevent the generation of harmful or offensive content, ensuring responsible and ethical use. This versatile model goes beyond mere text generation; ShiningValiant can be finetuned for specific tasks, ranging from answering questions to code generation and creative writing. 

Furthermore, its multimodal capabilities extend to processing and generating text, code, and images, making ShiningValiant a valuable asset across various applications.

Use Cases and Application

• Education: Facilitate personalized learning, answer student queries, and provide feedback with advanced language models.
• Creative Content Generation: Generate diverse content, including poems, code, scripts, musical pieces, email, and letters using innovative language models.
• Customer Service: Enhance customer service by responding to queries, offering tailored product recommendations, and efficiently resolving issues.
• Research: Utilize language models for generating hypotheses, analyzing data, and assisting in the writing of research papers.
• Entertainment: Create interactive stories, offer personalized recommendations, and provide companionship through advanced language models.

Click here to explore this LLM on hugging face.

Falcon-RW-1B-INSTRUCT-OpenOrca

Falcon-RW-1B-Instruct-OpenOrca is a potent large language model (LLM) with 1 billion parameters. Trained on the Open-Orca/SlimOrca dataset and rooted in the Falcon-RW-1B model, this LLM undergoes a fine-tuning process that significantly enhances its prowess in instruction-following, reasoning, and factual language tasks.

Key features include a Causal Decoder-Only mechanism, allowing it to efficiently generate text, translate languages, and provide informative answers to questions. This model also demonstrates superior excellence in its domain, securing the top spot as the #1 ranking model on the Open LLM Leaderboard within the ~1.5B parameters category.

Use Cases and Application

• Question Answering: Provides comprehensive and informative answers to open-ended, challenging, or strange questions.
• Creative Text Generation: Generates various creative text formats, including poems, code, scripts, musical pieces, emails, letters, etc.
• Instruction Following: Completes requests thoughtfully by following instructions precisely.
• Factual Language Tasks: Demonstrates strong capabilities in tasks requiring factual knowledge and reasoning.
• Translation: Accurately translates languages, facilitating communication and information access across languages.

You can access this Large Language Model on hugging face using this link.

Conclusion

Hugging Face’s repository of large language models opens up a world of possibilities for developers, researchers, and enthusiasts. These models contribute significantly to advancing natural language understanding and generation with their varying architectures and capabilities. As technology continues to evolve, these models’ potential applications and impact on diverse fields are boundless. The journey of exploration and innovation in the realm of Large Language Models continues, promising exciting developments in the future.

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