Breaking AI News: Discover My Top Pick!

Table of Contents

1. Introduction
2. Open Hermes 2 Model
   1. What is Open Hermes 2?
   2. Training Data and Benchmark Performance
   3. Pros and Cons
3. Open Hermes 2.5 Model
   1. Introduction to Open Hermes 2.5
   2. Fine-Tuning for Coding Tasks
   3. Benchmark Performance Comparison
   4. Pros and Cons
4. Yan Mistal 7 Billion Parameter Model
   1. Context Window and Perplexity
   2. Usage in Retrieval Tasks
   3. Benefits and Limitations
5. Deep Seek Coder Model
   1. Overview of Deep Seek Coder
   2. Coding-Specific Features
   3. Supported Programming Languages
   4. Applications and Potential
6. OpenAI Codex Models
   1. Transition from Codex Models to GP4
   2. Comparison with Other Models
   3. Benefits and Considerations
7. Distill Whisper Model
   1. Distilled Version of Whisper Model
   2. Benefits of Efficient Transcription
   3. Integration with Deep Diffusers
8. Latent Consistency Models
   1. Introduction to Latent Consistency Models (LCM)
   2. Faster Inference and Improved Results
   3. Use Cases and Future Development
9. OpenChart Model
   1. Improved Version for Chat Applications
   2. Alignment Strategy: CRLF
   3. Performance and Integration
10. Baklava Model
    1. Multimodal Model Based on Lava
    2. Architecture and Training Process
    3. Open Source Availability
11. AutoAwq Library
    1. Activation Aware Weight Quantization
    2. Integration with Hugging Face Transformers
    3. Supported Models and Benefits
12. NefTune for Efficient Fine-Tuning
    1. Noisy Embeddings for Robustness
    2. Performance Improvement and Usage
13. AI Safety Summit and Executive Orders
    1. UK AI Safety Summit
    2. US Executive Order on AI Regulation
14. Remarks from Yan Liu and Elon Musk
    1. Doomsday Scenario: AI Power
    2. Introduction of Gro Model

Open Hermes 2 Model

The Open Hermes 2 model is one of the latest additions to the Hermes series introduced by Tium. With 7 billion parameters, this model is considered state-of-the-art and has received positive feedback from the AI community. It is a versatile multi-purpose model that can be used for various tasks, including chat about programming, food, and more. The model has been fine-tuned on 900,000 entries primarily generated from GPD4 data. Open Hermes 2 demonstrates impressive performance, as reflected in its benchmark results alongside other 70 billion parameter models.

Open Hermes 2 Pros:

• State-of-the-art model with 7 billion parameters
• Versatile and multi-purpose
• Impressive benchmark results

Open Hermes 2 Cons:

• Limited fine-tuning on specific domains or tasks

Open Hermes 2.5 Model

Open Hermes 2.5 is an extension of the Open Hermes 2 model, specifically fine-tuned for programming tasks. While Open Hermes 2 is a general-purpose model, Open Hermes 2.5 focuses on coding-related tasks and exhibits superior performance compared to its predecessor. Through its fine-tuning process for programming, the model has achieved significant improvements, including benchmark results and overall coding capability. Despite a slight reduction in one benchmark score, the model shows a considerable net gain and outperforms other coding models, including Codex.

Open Hermes 2.5 Pros:

• Specialized for coding tasks
• Significant improvements in coding benchmarks
• Comparable performance to Codex models

Open Hermes 2.5 Cons:

• Limited application outside of coding-related tasks

Yan Mistal 7 Billion Parameter Model

The Yan Mistal model, developed by researchers at Newest, is a powerful model with 7 billion parameters and a notable context window of 128k. The model's perplexity, a measure of its ability to generate coherent text, remains impressively low even beyond 120,000 tokens. While the model primarily focuses on retrieval tasks, it can also be utilized for text generation. With its extensive context window and real-time knowledge from platforms like xPlatform, the model proves useful for information synthesis and retrieval, particularly in scenarios requiring longer contexts.

Yan Mistal Pros:

• Low perplexity and coherence in text generation
• Well-suited for retrieval tasks
• Extensive context window for longer contexts

Yan Mistal Cons:

• Limited support for non-retrieval tasks

Deep Seek Coder Model

The Deep Seek Coder model, developed by Deep Seek AI, is a coding-specific model series comprising various models with parameter ranges from 1 billion to 33 billion. It has been trained on English and Chinese pre-trained data sets, featuring up to 2 trillion tokens. With a 16k window size and the ability to perform fill-in-the-blank tasks, this model enables developers to generate code snippets quickly, making it suitable for code completion tasks. The Deep Seek Coder model exhibits superior performance and extensive support for programming languages such as Java, C++, Python, JavaScript, and more.

Deep Seek Coder Pros:

• Coding-specific model series
• Supports various programming languages
• Efficient code generation and completion

Deep Seek Coder Cons:

• Limited support for languages other than those emphasized

OpenAI Codex Models

OpenAI's Codex models, released as an open source initiative, aim to provide powerful language models that match or surpass the performance of previous models. While Codex models were initially designed to be an alternative to GPT-3.5 turbo, it was later concluded that these models are not as effective as GP4. However, Codex models still serve as excellent options, especially considering their open source nature and comparability to GPT-3.5 turbo. Notably, Codex models were designed to be fine-tuned on code instruction data, allowing for improved coding-related tasks.

Codex Pros:

• Open source initiative
• Comparable performance to GPT-3.5 turbo
• Fine-tuning capability for code instruction tasks

Codex Cons:

• Lower performance compared to GP4 models

Distill Whisper Model

Distill Whisper is a distilled version of the Whisper model developed by OpenAI. This model focuses on efficient transcription, making it suitable for applications where real-time or near-real-time transcription is required. With optimizations such as flash Attention support and efficient transformers, this model ensures fast and accurate transcription even on resource-constrained devices. Due to its reduced model size, it facilitates easy deployment and integration in various domains, providing reliable and convenient transcription capabilities.

Distill Whisper Pros:

• Efficient and accurate transcription
• Real-time transcription support
• Compatibility with resource-constrained devices

Distill Whisper Cons:

• Limited to English language transcription

Latent Consistency Models

Latent Consistency Models (LCM) are a next generation of generative models, building upon the Stable Diffusion models. LCMs offer significantly faster image synthesis with just a few steps of inference, making them ideal for generating coherent and high-resolution images. They can be distilled from pre-trained stable diffusion models in just 4,000 steps using 32 A100 GPUs. LCMs demonstrate their ability to generate 768x768 resolution images with remarkable consistency. Their applications include image-to-image generation tasks, real-time image synthesis, and more.

Latent Consistency Models Pros:

• Fast image synthesis with minimal inference steps
• High-resolution image generation
• Real-time applications and improved performance

Latent Consistency Models Cons:

• Limited to image generation tasks

OpenChart Model

OpenChart is a versatile chat model designed as a replacement for ChatGPT. It utilizes the CRLF strategy (Correction, Redirection, Many Linford) for alignment and fine-tuning, resulting in improved model performance. By leveraging maximum likelihood estimates (MLE), OpenChart delivers enhanced results and better benchmarks. This model has been tested extensively, including popular benchmarks like GPT, OpenPlatypus, and others. OpenChart offers improved chat capabilities and serves as a valuable open source alternative for chat-related applications.

OpenChart Pros:

• Improved chat model performance
• Effective alignment and fine-tuning strategy
• Excellent benchmarks results

OpenChart Cons:

• Limited to chat applications, not suitable for other domains

Baklava Model

Baklava is a multimodal model based on the Lava model. By modifying the training process, custom datasets, and architecture, Baklava presents an improved version of the original Lava implementation. With open source availability and model weights provided by Skunk Works AI, Baklava offers enhanced performance and capabilities. The model supports various sizes, including a 13 billion parameter model, providing flexibility in its application. Baklava demonstrates excellent performance across multiple benchmarks, showcasing its capabilities for diverse use cases.

Baklava Pros:

• Based on Lava with enhanced performance
• Customizable model sizes and weights
• Open source availability

Baklava Cons:

• Requires training on custom datasets

AutoAwq Library

AutoAwq is an automatic quantization library that simplifies the utilization of activation-aware weight quantization (Awq) models. With support for various popular models, including Vuna-M, Llama, Lama 2, Falcon, GPT-U, Opt, and MPT, AutoAwq offers an easy way to incorporate Awq models into existing workflows. The library has been merged into the Hugging Face Transformers model, allowing seamless integration for quick usage. By achieving superior results, Awq models facilitate efficient inference on consumer hardware, making them ideal for deployment in resource-constrained environments.

AutoAwq Pros:

• Simplified usage of Awq models
• Integration with Hugging Face Transformers
• Enhanced performance on consumer hardware

AutoAwq Cons:

• Limited to supported models and frameworks

NefTune for Efficient Fine-Tuning

NefTune introduces the concept of noisy embeddings to improve the robustness of fine-tuned models. By artificially introducing noise, NefTune enhances instruction fine-tuning, leading to better performance on various tasks. The use of noisy embeddings has showcased significant improvements in benchmark results and overall model performance. By taking AdVantage of NefTune, models achieve higher accuracy and demonstrate better generalization. This technique can be easily applied using the Hugging Face Trainer by specifying the noise level parameter during fine-tuning.

NefTune Pros:

• Improved robustness through noisy embeddings
• Enhanced benchmark results and performance
• Easy application with Hugging Face Trainer

NefTune Cons:

• Requires fine-tuning implementation with noisy embeddings

AI Safety Summit and Executive Orders

The UK government organized the AI Safety Summit, bringing together key figures from the AI community, policymakers, and experts to discuss AI regulations. The aim of the summit was to address the challenges and potential risks associated with AI and identify ways to ensure safe and responsible development and use of AI technologies.

The US government also issued an executive order focusing on the safe, secure, and trustworthy development and use of artificial intelligence. The order emphasizes the need for transparency, public accountability, and the responsible deployment of AI systems.

AI Safety Summit and Executive Orders Pros:

• Acknowledgment of the importance of AI regulation
• Facilitation of discussions among industry experts
• Promotion of responsible AI development

AI Safety Summit and Executive Orders Cons:

• Potential impact on open source initiatives and innovations

Remarks from Yan Liu and Elon Musk

Yan Liu, head of Meta AI Research, expressed concerns about AI power and the potential risks associated with its unchecked growth. He highlighted that the real doomsday scenario lies in the concentration of power in the hands of a few entities and emphasized the importance of open sourcing AI technology to ensure equitable access and responsible development.

Elon Musk, founder of X Twitter, introduced a new AI model called "Gro" inspired by "The Hitchhiker's Guide to the Galaxy." Gro is designed to answer almost any question and inject some humor into its responses. The model is still in its early stages but shows potential for improvement. Gro's availability will be tied to X Twitter's new premium plus subscription tier, attracting users with a variety of features, including access to Gro's capabilities.

Remarks from Yan Liu and Elon Musk Pros:

• Advocacy for open source AI technology
• Push for equitable access and responsible development
• Introduction of Novel AI models with unique features

Remarks from Yan Liu and Elon Musk Cons:

• Uncertainty surrounding commercial availability