Exploring Llama LLM and Its all Variants: A Revolution in Language Generation

Introduction:

In the realm of natural language processing (NLP) and text generation, Llama LLM has emerged as a notable contender. Developed by Facebook Research, Llama LLM offers a range of models with varying capacities and efficiencies. In this blog post, we will dive into the world of Llama LLM and explore its different variants developed by various companies and developers.

Llama LLM by Facebook Research: Llama LLM is a text generation model developed by Facebook Research. It has gained significant attention due to its impressive pretrained weights and its ability to generate coherent and contextually relevant text. Facebook Research has released four different models as part of the Llama series, each with increasing capacity and efficiency.

Llama 7b: Llama 7b is the first variant in the Llama series, possessing pretrained weights with approximately 7 billion parameters. Although smaller in scale compared to subsequent models, Llama 7b is still capable of generating coherent and contextually relevant text. It serves as an entry point for developers and researchers to experiment with the Llama architecture.

Llama 13b: Building upon the success of Llama 7b, Facebook Research introduced Llama 13b, a model with pretrained weights containing around 13 billion parameters. The leap from 7 billion to 13 billion parameters significantly enhances Llama's text generation capabilities. Remarkably, Llama 13b is comparable to OpenAI's GPT-3, which boasts a staggering 175 billion parameters.

Llama 30b: Continuing the trend of scaling up, Llama 30b takes the Llama series to new heights. With pretrained weights containing approximately 30 billion parameters, Llama 30b pushes the boundaries of text generation even further. Its expanded capacity allows for more nuanced and contextually accurate responses, making it a powerful tool for a wide range of natural language processing tasks.

Llama 65b: The latest addition to the Llama family, Llama 65b, is the culmination of Facebook Research's efforts in creating highly advanced text generation models. With a colossal 65 billion parameters, Llama 65b represents the pinnacle of Llama's capacity and efficiency. This model demonstrates remarkable advancements in generating coherent, context-aware text, enabling it to excel in a multitude of language-based applications.

Vicuna-13B: In addition to the Llama variants developed by Facebook Research, the Vicuna team has contributed to the Llama family with their variant, Vicuna-13B. Vicuna-13B is an open-source chatbot trained by fine-tuning Llama on user-shared conversations collected from ShareGPT. This fine-tuning process has enabled Vicuna-13B to achieve impressive results in terms of quality and performance. Preliminary evaluations using GPT-4 as a judge indicate that Vicuna-13B achieves over 90% quality compared to models like OpenAI's ChatGPT and Google Bard, while outperforming other models like Llama and Stanford Alpaca in over 90% of cases. Its trained on 300 dollars

Wizard LM - 13b / 30b: WizardLM is another variant of Llama developed by NLPxUCAN. It is an instruction-following language model designed to empower large pre-trained language models to follow complex instructions. WizardLM utilizes the Evol-Instruct technique, allowing it to generate text that adheres to specific instructions provided by the user. This capability opens up possibilities for creating interactive and responsive systems that can understand and execute complex tasks based on user instructions.

Guanaco: Guanaco is a variant of Llama that introduces QLoRA, an efficient finetuning approach aimed at reducing memory usage while preserving full 16-bit finetuning task performance. By backpropagating gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters (LoRA), Guanaco achieves exceptional results. In fact, Guanaco outperforms all previously released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT. Impressively, this is achieved with only 24 hours of finetuning on a single GPU, thanks to the memory-saving innovations of QLoRA.

Koala: Koala is a variant of Llama developed by BAIR, focusing on creating a dialogue model for academic research. Koala is trained through fine-tuning Meta's Llama on dialogue data gathered from the web. The model's training process and dataset curation are described, and the results of a user study comparing Koala to ChatGPT and Stanford's Alpaca are presented. The study shows that Koala effectively responds to various user queries, generating preferred responses compared to Alpaca and achieving at least comparable performance to ChatGPT in over half of the cases.

Alpaca: Alpaca is a variant of Llama developed by Stanford, specifically tailored for instruction-following tasks. It is fine-tuned from Meta's LLaMA 7B model using instruction-following demonstrations generated in the style of self-instruct using OpenAI's text-davinci-003 model. Alpaca demonstrates similar behaviors to text-davinci-003 while being surprisingly small and easy/cheap to reproduce.

StableLM: StableLM is another noteworthy variant developed by Stability AI. These models are trained on a new dataset that builds on The Pile, a massive collection of text data containing 1.5 trillion tokens, making it roughly three times the size of The Pile. The StableLM models are trained on up to 1.5 trillion tokens, resulting in enhanced contextual understanding and text generation capabilities.

Tulu 7B / 13B : A Finetuned LLaMa Variant for Instruction-following Tasks. Tulu is an intriguing variant of LLaMa developed through fine-tuning the 7B LLaMa model on a diverse range of instruction datasets. These datasets include FLAN V2, CoT, Dolly, Open Assistant 1, GPT4-Alpaca, Code-Alpaca, and ShareGPT, enabling Tulu to specialize in instruction-following tasks across multiple domains.

By leveraging these instruction datasets, Tulu enhances its understanding and execution of complex instructions, making it a valuable tool for tasks that require precise instruction-following capabilities. The finetuning process ensures that Tulu adapts to the nuances and requirements of different instructions, leading to improved performance and accuracy.

Now we will only provide name of all other variants

1. selfee 7B / 13B
2. camel 7B / 13B
3. samantha 7B / 13B
4. airoboros 7B / 13B
5. gpt4 alpaca lora mlp 7B / 13B /30B / 65B
6. gorilla-llm 7B / 13B
more will come here.

Conclusion:

The emergence of Llama LLM and its various variants has sparked a revolution in the field of language generation and natural language processing (NLP). These models, developed by companies and researchers from different domains, have pushed the boundaries of text generation capabilities, enabling more coherent, contextually relevant, and instruction-following responses.

Facebook Research's Llama series serves as the foundation for this revolution, starting with Llama 7b and progressively scaling up to Llama 13b, Llama 30b, and the latest addition, Llama 65b. Each variant demonstrates impressive advancements in text generation, rivaling even the renowned OpenAI's GPT-3 model.

Outside of Facebook Research, the Llama family expands with contributions from other teams. Vicuna-13B, developed by the Vicuna team, showcases exceptional quality and performance through fine-tuning on user-shared conversations. WizardLM, developed by NLPxUCAN, empowers large pre-trained models to follow complex instructions, opening new possibilities for interactive systems. Guanaco introduces QLoRA, a memory-saving approach, and achieves outstanding results, surpassing previously released models. Koala, developed by BAIR, focuses on creating a dialogue model for academic research, showing promising results in user studies. Alpaca, developed by Stanford, specializes in instruction-following tasks and demonstrates behaviors similar to larger models but in a smaller and more cost-effective package. StableLM, developed by Stability AI, leverages a massive dataset to enhance contextual understanding and text generation capabilities. Tulu, another finetuned Llama variant, excels in instruction-following tasks across multiple domains.

These Llama variants are just the beginning, with more exciting models on the horizon, including Selfee, Camel, Samantha, Airoboros, GPT4 Alpaca LoRA MLP, and Gorilla-LLM. The continuous development and innovation in the Llama family promise even more advancements in language generation and NLP.

In conclusion, the exploration of Llama LLM and its variants has paved the way for a revolution in language generation. These models have demonstrated remarkable capabilities, pushing the boundaries of text generation and opening up new possibilities for various applications. The future holds immense potential for further advancements and improvements, as the Llama family continues to evolve and expand.