Model Benchmarking: Unveiling Insights into Language Models

Recently, the language model community has been buzzing with discussions about the performance of various models. A particular model that caught our attention is Beyonder, which, in casual testing, seems to be one of the rare non-broken Mixture of Experts (MoEs). It incorporates openchat-3.5, a model previously benchmarked by the community.

But what's the best inference engine? This question often arises, and it's crucial to consider the source code for testing methods. Without transparency, rankings lack validity and reproducibility.

One frustration echoed in the community is the lack of support for fine-tuning Mixtral, leaving many to grapple with its intricacies. This sentiment highlights the importance of collaborative efforts in the open-source model tuning community.

While some models like Solar and its variants didn't meet expectations, there are hidden gems like **SOLAR 10.7b Instruct v1.0 uncensored**. Finetuned on the **Toxic DPO** dataset, it outshines others in various tests (source).

As the community dives deeper into quantization methods, questions about hf bitsandbytes 4-bit quantization arise. The choice between nf4 or fp4 and the impact on results sparks curiosity. It's suggested to explore alternatives like gguf for potentially enhanced quality.

Concerns about Mixtral_34Bx2_MoE_60B's performance and its response to specific queries add an interesting layer to the ongoing discussions. Users eagerly await the release of Mixtral_34Bx2_MoE_60B GGUF for further exploration.

Unexpected findings, such as the difference between bagel-34b-v0.2 and bagel-dpo-34b-v0.2, challenge assumptions about the impact of DPO on model performance.

While model rankings are valuable, concerns are raised about the adequacy of the test set. The number of multiple-choice questions may need reconsideration to ensure a more robust evaluation of models.

Amidst the complexities, questions arise about models passing the single letter test. Tokenization nuances are considered, leading to inquiries about the efficacy of models like Mixtral_34bx2 compared to Mistral Medium.

As the rankings shift and new models emerge, the community eagerly anticipates developments in the open-source domain. Continuous improvements underscore the dynamic nature of language models.

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

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

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 … 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

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

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

Extending Context Size in Language Models

Language models have revolutionized the way we interact with artificial intelligence systems. However, one of the challenges faced is the limited context size that affects the model's understanding and response capabilities.

In the realm of natural language processing, attention matrices play a crucial role in determining the influence of each token within a given context. This cross-correlation matrix, often represented as an NxN matrix, affects the overall model size and performance.

One possible approach to overcome the context size limitation … 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

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