Comparing Large Language Models: WizardLM 7B, Alpaca 65B, and More

A recent comparison of large language models, including WizardLM 7B, Alpaca 65B, Vicuna 13B, and others, showcases their performance across various tasks. The analysis highlights how the models perform despite their differences in parameter count. The GPT4-X-Alpaca 30B model, for instance, gets close to the performance of Alpaca 65B. Furthermore, the Vicuna 13B and 7B models demonstrate impressive results, given their lower parameter numbers.

Some users inquired about additional models, such as "oasst-sft-6-llama-30b" and the ChatGPT models, requesting that they be added to the comparison. Memory usage and performance of GPT4-X-Alpaca 30B were also queried, specifically regarding its compatibility with 48GB VRAM. Users shared their experience with the different models, with some expressing interest in comparing them to GPT4 and discussing the potential of Vicuna 30B when it becomes available.

Overall, the community continues to engage with these powerful language models, sharing insights and results to further advance the understanding and applications of these technologies.

Language Models WizardLM 7B Alpaca 65B GPT4-X-Alpaca 30B Vicuna 13B

Building Language Models for Low-Resource Languages

As the capabilities of language models continue to advance, it is conceivable that "one-size-fits-all" model will remain as the main paradigm. For instance, given the vast number of languages worldwide, many of which are low-resource, the prevalent practice is to pretrain a single model on multiple languages. In this paper, the researchers introduce the Sabiá: Portuguese Large Language Models and demonstrate that monolingual pretraining on the target language significantly improves models already extensively trained on diverse corpora. Few-shot evaluations … click here to read

Reimagining Language Models with Minimalist Approach

The recent surge in interest for smaller language models is a testament to the idea that size isn't everything when it comes to intelligence. Models today are often filled with a plethora of information, but what if we minimized this to create a model that only understands and writes in a single language, yet knows little about the world? This concept is the foundation of the new wave of "tiny" language models .

A novel … click here to read

Navigating Language Models: A Practical Overview of Recommendations and Community Insights

Language models play a pivotal role in various applications, and the recent advancements in models like Falcon-7B, Mistral-7B, and Zephyr-7B are transforming the landscape of natural language processing. In this guide, we'll delve into some noteworthy models and their applications.

Model Recommendations

When it comes to specific applications, the choice of a language model can make a significant difference. Here are … click here to read

WizardLM: An Efficient and Effective Model for Complex Question-Answering

WizardLM is a large-scale language model based on the GPT-3 architecture, trained on diverse sources of text, such as books, web pages, and scientific articles. It is designed for complex question-answering tasks and has been shown to outperform existing models on several benchmarks.

The model is available in various sizes, ranging from the smallest version, with 125M parameters, to the largest version, with 13B parameters. Additionally, the model is available in quantised versions, which offer improved VRAM efficiency without … click here to read

Re-Pre-Training Language Models for Low-Resource Languages

Language models are initially pre-trained on a huge corpus of mostly-unfiltered text in the target languages, then they are made into ChatLLMs by fine-tuning on a prompt dataset. The pre-training is the most expensive part by far, and if existing LLMs can't do basic sentences in your language, then one needs to start from that point by finding/scraping/making a huge dataset. One can exhaustively go through every available LLM and check its language abilities before investing in re-pre-training. There are surprisingly many of them … click here to read

Automated Reasoning with Language Models

Automated reasoning with language models is a fascinating field that can test reasoning skills. Recently, a model named Supercot showed accidental proficiency in prose/story creation. However, it's essential to use original riddles or modify existing ones to ensure that the models are reasoning and not merely spewing out existing knowledge on the web.

Several models have been tested in a series of reasoning tasks, and Vicuna-1.1-Free-V4.3-13B-ggml-q5_1 has been tested among others. It performed well, except for two coding points. Koala performed slightly better … click here to read

Local Language Models: A User Perspective

Many users are exploring Local Language Models (LLMs) not because they outperform ChatGPT/GPT4, but to learn about the technology, understand its workings, and personalize its capabilities and features. Users have been able to run several models, learn about tokenizers and embeddings , and experiment with vector databases . They value the freedom and control over the information they seek, without ideological or ethical restrictions imposed by Big Tech. … click here to read

Meta's Fairseq: A Giant Leap in Multilingual Model Speech Recognition

AI and language models have witnessed substantial growth in their capabilities, particularly in the realm of speech recognition. Spearheading this development is Facebook's AI team with their Multilingual Model Speech Recognition (MMS) , housed under the Fairseq framework.

Fairseq, as described on its GitHub repository , is a general-purpose sequence-to-sequence library. It offers full support for developing and training custom models, not just for speech recognition, … click here to read

Max Context and Memory Constraints in Bigger Models

One common question that arises when discussing bigger language models is whether there is a drop-off in maximum context due to memory constraints. In this blog post, we'll explore this topic and shed some light on it.

Bigger models, such as GPT-3.5, have been developed to handle a vast amount of information and generate coherent and contextually relevant responses. However, the size of these models does not necessarily dictate the maximum context they can handle.

The memory constraints … click here to read

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