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 some notable recommendations:

Intel's SlimOrca is a dataset that has proven to be effective, particularly in specific domains. It's fine-tuned based on mistralai/Mistral-7B-v0.1 on the open-source dataset Open-Orca/SlimOrca. The fine-tuning process includes alignment with the DPO algorithm. For more details, you can refer to Intel's blog: The Practice of Supervised Fine-tuning and Direct Preference Optimization on Habana Gaudi2.
For ERP tasks, Toppy stands out as an excellent choice.
If you are seeking structured outputs, especially for coding or instructional content, models like Zephyr 7B and Dolphin 2.2.1 are recommended.
OpenChat 3.5 consistently performs well across various metrics, making it a reliable choice.
For coding tasks, consider exploring DeepSeek Coder Instruct 6.7B.
Orca2 7B, recently released, competes strongly with OpenHermes 2.5, according to user feedback.

Community Insights

The user community provides valuable insights into the performance of different models. According to discussions:

This heuristic benchmark compares 7B and 13B models on various tasks, offering a comprehensive overview.
OpenHermes 2.5 is widely regarded as a top choice for various tasks, including coding.
While personal preferences vary, Mistral and OpenOrca also have a dedicated user base.

Choosing the right language model depends on the specific requirements of your task. It's advisable to experiment with different models and evaluate their performance in your context.

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

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

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

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

Exploring Pygmalion: The New Contender in Language Models

Enthusiasm is building in the OpenAI community for Pygmalion , a cleverly named new language model. While initial responses vary, the community is undeniably eager to delve into its capabilities and quirks.

Pygmalion exhibits some unique characteristics, particularly in role-playing scenarios. It's been found to generate frequent emotive responses, similar to its predecessor, Pygmalion 7B from TavernAI. However, some users argue that it's somewhat less coherent than its cousin, Wizard Vicuna 13B uncensored, as it … click here to read

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 … click here to read

Exploring GPT-4, Prompt Engineering, and the Future of AI Language Models

In this conversation, participants share their experiences with GPT-4 and language models, discussing the pros and cons of using these tools. Some are skeptical about the average person's ability to effectively use AI language models, while others emphasize the importance of ongoing learning and experimentation. The limitations of GPT-4 and the challenges in generating specific content types are also highlighted. The conversation encourages open-mindedness and empathy towards others' experiences with AI language models. An official … 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

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