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Un nuevo estudio desafía los modelos de formación de planetas a través de la ambigüedad química del PDS 70b

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Una nueva investigación sobre PDS 70b, un exoplaneta que se forma a unos 400 años luz de distancia en la constelación Centauri, sugiere que los modelos prevalecientes de exoplanetas planeta La configuración puede requerir revisión. Según un estudio publicado en Astrophysical Journal Letters, los astrónomos encontraron un desajuste en la composición química del planeta. Atmósfera Y el disco protoplanetario circundante del que surgió. Este descubrimiento ha llevado a los investigadores a reconsiderar las teorías establecidas sobre cómo los planetas acumulan su masa y elementos durante su formación.

Características únicas del PDS 70b

El planeta, parte de un sistema de dos planetas, tiene casi tres veces el tamaño de Júpiter y orbita su estrella anfitriona a una distancia similar a la posición de Urano en el sistema solar. Investigadores Creemos que PDS 70b ha estado recolectando material durante unos 5 millones de años y puede estar acercándose al final de su fase de formación. Utilizando el telescopio Keck 2 en Hawaii, los científicos escanearon la atmósfera del planeta en busca de monóxido de carbono y agua, proporcionando información sobre el carbono y los elementos que se encuentran en él. Oxígeno Niveles: los principales indicadores de los orígenes planetarios.

Inconsistencia en la composición química.

Los resultados revelaron que la atmósfera del planeta contiene cantidades mucho menores Carbón Y oxígeno del esperado. Según el Dr. Chih-Chun Hsu, investigador postdoctoral de la Universidad Northwestern y autor principal del estudio, en un comunicado, esta discrepancia pone de relieve una posible simplificación excesiva en los modelos ampliamente aceptados de formación planetaria.

Teorías detrás de resultados inesperados

Los investigadores sugirieron dos posibles explicaciones. Una sugiere que PDS 70b incorporó la mayor parte del carbono y el oxígeno de materiales sólidos como el hielo y el polvo, liberando estos elementos durante la evaporación antes de incorporarse al planeta. Este proceso puede cambiar drásticamente la proporción de carbono a oxígeno, señaló en un comunicado el Dr. Jason Wang, profesor asistente de la Universidad Northwestern y coautor del estudio. Alternativamente, el disco protoplanetario puede haber experimentado recientemente un enriquecimiento de carbono, un escenario respaldado por algunos modelos de formación.

Se espera que futuras observaciones del segundo planeta del sistema, PDS 70c, proporcionen más datos para mejorar la comprensión de los procesos de formación planetaria. Los científicos subrayan la necesidad de estudiar más sistemas similares para obtener conocimientos más amplios sobre los mecanismos de formación de planetas.

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Meta’s new CodeLlama 70B performance tested

Meta's new CodeLlama 70B performance tested

Meta AI has this week released CodeLlama 70B a new large language model specifically designed to assist developers and coders.  The new AI coding model has and impressive 70 billion parameters but is capable of being run locally. This model is designed to handle a wide range of tasks, from language processing to complex problem-solving. It’s a sophisticated tool that’s capturing the attention of developers and businesses alike. But how does it compare to other AI models, such as the Deep Seek Coder, which has 33 billion parameters? Let’s dive into a detailed performance evaluation of these two AI powerhouses.

When you first start working with CodeLlama 70B, you’ll notice it’s not as straightforward as some other models. It has a unique way of interpreting prompts, which means you’ll need to spend some time getting used to its system. The model uses a tokenizer to translate your input into a format it can understand, which is crucial for getting the most out of its capabilities. This includes learning how to use new source tokens and a ‘step’ token that helps with message formatting. Mastering these elements is essential if you want to fully leverage what CodeLlama 70B has to offer.

CodeLlama 70B performance tested

However, the advanced nature of CodeLlama 70B comes with its own set of demands, particularly when it comes to hardware. The model’s size means it needs a lot of VRAM, which could require you to invest in more powerful equipment or consider renting server space. This is an important consideration for anyone thinking about integrating this model into their workflow. Despite these requirements, CodeLlama 70B is exceptional when it comes to generating structured responses that are in line with validation data. check out the performance testing of CodeLlama 70B kindly carried out by Trelis Research providing a fantastic overview of what you can expect from the latest large language model to be rolled out by Meta AI.

Here are some other articles you may find of interest on the subject of Llama AI models :

When we put CodeLlama 70B to the test with specific tasks, such as reversing letter sequences, creating code, and retrieving random strings, the results were mixed. The model has built-in safeguards to ensure that outputs are safe and appropriate, but these can sometimes restrict its performance on certain tasks. However, these safety features are crucial for maintaining the model’s overall reliability.

For those who are interested in using CodeLlama 70B, it’s a good idea to start with smaller models. This approach allows you to create a more manageable environment for testing and development before you tackle the complexities of CodeLlama 70B. This model is really meant for production-level tasks, so it’s important to be prepared. Fortunately, there are resources available, such as one-click templates and a purchasable function calling model, that can help ease the transition.

CodeLlama 70B stands out in the field of AI for its advanced capabilities and its strong performance in adhering to validation data. However, the practical challenges it presents, such as its size and VRAM requirements, cannot be overlooked. By beginning with smaller models and utilizing available resources, you can prepare yourself for working with CodeLlama 70B. This will help ensure that your projects meet the highest quality standards and that you can make the most of this powerful AI tool.

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Run CodeLlama 70B locally for free for AI coding

Run CodeLlama 70B locally for free for AI coding

Meta AI has recently introduced a new coding language model known as CodeLlama 70B, which is making significant strides in the way developers write and understand code. This advanced tool has achieved an impressive score on the HumanEval benchmark, indicating its high level of performance in code synthesis and comprehension. For developers looking to streamline their coding process, CodeLlama 70B is emerging as an essential resource and offers users a large language model that can use text prompts to generate and discuss code.

Code Llama is a family of state-of-the-art, open-access versions of Llama 2 specialized on code tasks. Code Llama has been released with the same permissive community license as Llama 2 and is available for commercial use and is available in 7B, 13B,  34B and 70B model sizes over on GitHub.

The core strength of CodeLlama 70B lies in its ability to generate code that is both contextually accurate and coherent. This is made possible by its autoregressive mechanism and an optimized Transformer architecture, which are at the forefront of natural language processing technology. The model’s sophisticated design allows it to understand and produce code in a way that closely mirrors human coding practices.

Installing CodeLlama to run on your local PC

What sets CodeLlama 70B apart from other coding tools is its adaptability to various coding requirements. The model comes in three distinct versions, each designed to cater to different developer needs. The base model, CodeLlama, is skilled in general code generation and understanding. For those who specialize in Python, CodeLlama Python is fine-tuned to enhance coding in that language. Lastly, CodeLlama Instruct is tailored for tasks that demand strict adherence to instructions and a focus on secure coding.

Here are some other articles you may find of interest on the subject of running artificial intelligence AI models locally on your home PC or business network.

In terms of handling complex and lengthy code, CodeLlama 70B is well-equipped. During its fine-tuning process, the model has been trained to manage up to 16,000 tokens and can support up to 100,000 tokens during inference. This allows it to efficiently process large blocks of code. Additionally, the model’s substantial parameter size gives it the flexibility to work with a variety of programming languages, further extending its utility to developers.

Ease of access and installation is another advantage of CodeLlama 70B. The model can be easily installed through LM Studio, which facilitates the local execution of large, open-source language models. For those who prefer online platforms, CodeLlama 70B is also available on Hugging Face, a repository known for its extensive range of pre-trained models. This dual availability ensures that developers can quickly incorporate CodeLlama 70B into their existing workflows, without significant downtime.

Meta AI’s CodeLlama 70B is a sophisticated coding language model that is poised to enhance the capabilities of developers across the board. It offers a range of solutions tailored to different programming needs, from general code generation to specialized Python development and secure coding. You can learn more about Code Llama from the research paper which is available.

With its user-friendly installation options and robust support for handling large code blocks, CodeLlama 70B stands out as a valuable addition to the developer’s toolkit. As the field of coding continues to evolve, tools like CodeLlama 70B are playing a crucial role in shaping the future of software development. If you’d like to request access to the next version of Llama jump over to the official Meta AI website where you can register your details.

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Code Llama 70B beats ChatGPT-4 at coding and programming

Code Llama 70B beats ChatGPT-4 at coding and programming

Developers, coders and those of you learning to program might be interested to know that the latest Code Llama 70B large language model released by Meta and specifically designed to help you improve your coding. Has apparently beaten OpenAI’s ChatGPT  when asking for coding advice, code snippets and coding across a number of different programming languages.

Meta AI recently unveiled Codellama-70B, the new sophisticated large language model (LLM) that has outperformed the well-known GPT-4 in coding tasks. This model is a part of the Codellama series, which is built on the advanced Lama 2 architecture, and it comes in three specialized versions to cater to different coding needs.

The foundational model is designed to be a versatile tool for a variety of coding tasks. For those who work primarily with Python, there’s a Python-specific variant that has been fine-tuned to understand and generate code in this popular programming language with remarkable precision. Additionally, there’s an instruct version that’s been crafted to follow and execute natural language instructions with a high degree of accuracy, making it easier for developers to translate their ideas into code. If you’re interested in learning how to run the new Code Llama 70B AI model locally on your PC check out our previous article

Meta Code Llama AI coding assistant

What sets Codellama-70B apart from its predecessors is its performance on the HumanEval dataset, a collection of coding problems used to evaluate the proficiency of coding models. Codellama-70B scored higher than GPT-4, marking a significant achievement for LLMs in the realm of coding. The training process for this model was extensive, involving the processing of a staggering 1 trillion tokens, focusing on the version with 70 billion parameters.

Here are some other articles you may find of interest on the subject of using artificial intelligence to help you learn to code or improve your programming skills.

The specialized versions of Codellama-70B, particularly the Python-specific and instruct variants, have undergone fine-tuning to ensure they don’t just provide accurate responses but also offer solutions that are contextually relevant and can be applied to real-world coding challenges. This fine-tuning process is what enables Codellama-70B to deliver high-quality, practical solutions that can be a boon for developers.

Recognizing the potential of Codellama-70B, Meta AI has made it available for both research and commercial use. This move underscores the model’s versatility and its potential to be used in a wide range of applications. Access to Codellama-70B is provided through a request form, and for those who are familiar with the Hugging Face platform, the model is available there as well. In an effort to make Codellama-70B even more accessible, a quantized version is in development, which aims to offer the same robust performance but with reduced computational requirements.

One of the key advantages of Codellama-70B is its compatibility with various operating systems. This means that regardless of the development environment on your local machine, you can leverage the capabilities of Codellama-70B. But the model’s expertise isn’t limited to simple coding tasks. It’s capable of generating code for complex programming projects, such as calculating the Fibonacci sequence or creating interactive web pages that respond to user interactions.

For developers and researchers looking to boost coding efficiency, automate repetitive tasks, or explore the possibilities of AI-assisted programming, Codellama-70B represents a significant step forward. Its superior performance on coding benchmarks, specialized versions for targeted tasks, and broad accessibility make it a valuable asset in the toolkit of any developer or researcher in the field of AI and coding. With Codellama-70B, the future of coding looks more efficient and intelligent, offering a glimpse into how AI can enhance and streamline the development process.

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Microsoft’s Orca-2 13B small language model outperforms 70B AI

Microsoft's Orca-2 13B small language model beats 70B alternatives

Microsoft has recently released a new research paper for its next generation Orca-2 AI model. Demonstrating that the power of artificial intelligence is not just reserved for the largest and most complex systems, but also thrives within more compact and accessible frameworks. Microsoft has made a bold stride in this direction with the introduction of Orca-2, a language model that challenges the prevailing notion that bigger always means better. This new development is particularly intriguing for those who are passionate about AI and seek to push the boundaries of what these systems can do.

Microsoft’s research paper, titled “Orca-2: Teaching Small Language Models How to Reason,” presents a fascinating exploration into how smaller models, like Orca-2, can be trained to enhance their reasoning abilities. With only 13 billion parameters, Orca-2 stands as a testament to the idea that the quality of training can significantly influence a model’s reasoning prowess. This is a crucial insight for anyone interested in the potential of smaller models to perform complex tasks that were once thought to be the exclusive domain of their larger counterparts. Microsoft explains a little more:

“Orca 2 is the latest step in our efforts to explore the capabilities of smaller LMs (on the order of 10 billion parameters or less). With Orca 2, we continue to show that improved training signals and methods can empower smaller language models to achieve enhanced reasoning abilities, which are typically found only in much larger language models.”

One of the most compelling aspects of Orca-2 is its ability to outperform models with up to 70 billion parameters in reasoning tasks. This is a testament to Microsoft’s innovative approach and is particularly relevant for those working within computational constraints or seeking more efficient AI solutions. The benchmark results of Orca-2 highlight the model’s proficiency in reasoning, which is a key element of advanced language comprehension.

Orca-2 small language model

Orca 2 comes in two sizes (7 billion and 13 billion parameters); both are created by fine-tuning the corresponding LLAMA 2 base models on tailored, high-quality synthetic data. We are making the Orca 2 weights publicly available to encourage research on the development, evaluation, and alignment of smaller LMs.

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Microsoft Orca-2

In a move that underscores their commitment to collaborative progress in AI, Microsoft has made Orca-2’s model weights available to the open-source community. This allows enthusiasts and researchers alike to tap into this state-of-the-art technology, integrate it into their own projects, and contribute to the collective advancement of AI.

The research paper goes beyond traditional imitation learning and introduces alternative training methods that endow Orca-2 with a variety of reasoning strategies. These methods enable the model to adapt to different tasks, indicating a more sophisticated approach to AI training. For those delving into the intricacies of AI, this represents an opportunity to explore new training paradigms that could redefine how we teach machines to think.

Orca-2’s training on a carefully constructed synthetic dataset has led to remarkable benchmark performances. This means that the model has been honed through strategic data use, ensuring its effectiveness and adaptability in real-world applications. For practitioners, this translates to a model that is not only powerful but also versatile in handling various scenarios.

The licensing terms for Orca-2 are tailored to emphasize its research-oriented nature. This is an important factor to consider when planning to utilize the model, as it supports a research-focused development environment and guides the application of Orca-2 in various projects.

Microsoft has also provided detailed instructions for setting up Orca-2 on a local machine. This allows users to tailor the model to their specific needs and gain a deeper understanding of its inner workings. Whether you’re a developer, researcher, or AI enthusiast, this level of customization is invaluable for exploring the full capabilities of Orca-2.

Microsoft’s Orca-2 represents a significant advancement for compact language models, offering enhanced reasoning capabilities that challenge the dominance of larger models. Engaging with Orca-2—whether through open-source collaboration, innovative training techniques, or research initiatives—places you at the forefront of a transformative period in AI development. Microsoft’s Orca-2 not only broadens the horizons for what smaller models can accomplish but also invites you to play an active role in this exciting field.

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New Zephyr-7B LLM fine-tuned, beats Llama-2 70B

New Zephyr-7B LLM fine-tuned Mistral-7B AI model

The world of artificial intelligence has witnessed another remarkable milestone with the release of the new Zephyr-7B AI model on Hugging Face. This innovative model is a fine-tuned successor to the original Mistral 7B, and it has managed to outperform larger 70 billion parameter models, even while being uncensored. The company has also unveiled a comprehensive technical report, offering a detailed overview of the training process of the model. Try out the Zephyr 7B Beta new here.

Direct preference optimization (DPO)

The Zephyr-7B model has been trained using a three-step strategy. The first step involves distilled supervised fine-tuning using the Ultra Chat dataset. This dataset, comprising 1.47 million multi-dialogues generated by GPT 3.5 Turbo, underwent a rigorous cleaning and filtering process, leaving only 200,000 examples. The distilled supervised fine-tuning process involves a teacher-student model dynamic, with a larger model like GPT 3.5 playing the role of the teacher and Zephyr-7B as the student. The teacher model generates a conversation based on a prompt, which is then used to fine-tune the student model, Zephyr-7B.

Zephyr-7B beats Llama-2 70B

The second step of the training strategy is AI feedback. This step utilizes the Ultra Feedback dataset, consisting of 64,000 different prompts. Four different models generate responses to each prompt, which are then rated by GP4 based on honesty and helpfulness. This process aids in refining the model’s responses, contributing to its overall performance.

Other articles we have written that you may find of interest on the subject of Zephyr and Mistral large language models:

The final step of the training strategy involves training another model using the dataset created with a winner and loser. This step further solidifies the learning of the Zephyr-7B model, ensuring that it can generate high-quality, reliable responses.

The performance of the Zephyr-7B model has been impressive, outperforming all other 7 billion models and even larger models like the Falcon 40 billion and Llama 2 70 billion models. However, it’s important to note that the model’s performance varies depending on the specific task. For instance, it lags behind in tasks like coding and mathematics. Thus, users should choose a model based on their specific needs, as the Zephyr-7B model may not be the best fit for all tasks.

Zephyr-7B LLM

One unique aspect of the Zephyr-7B model is its uncensored nature. While it is uncensored to a certain extent, it has been designed to advise against illegal activities when prompted, ensuring that it maintains ethical guidelines in its responses. This aspect is crucial in maintaining the integrity and responsible use of the model.

Running the Zephyr-7B model can be done locally using LMStudio or UABA Text Generation WebUI. This provides users with the flexibility to use the model in their preferred environment, enhancing its accessibility and usability.

The Zephyr-7B model is a significant addition to the AI landscape. Its unique training strategy, impressive performance, and uncensored nature set it apart from other models. However, its performance varies depending on the task at hand, so users should choose a model that best suits their specific needs. The company’s active Discord server provides a platform for discussions related to generative AI, fostering a community of learning and growth. As the field of AI continues to evolve, it will be exciting to see what future iterations of models like Zephyr-7B will bring.

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Llama 2 70B vs Zephyr-7B LLM models compared

Llama 2 70B vs Zephyr-7B LLM models compared

A new language model known as Zephyr has been created. The Zephyr-7B-α large language model, has been designed to function as helpful assistants, providing a new level of interaction and utility in the realm of AI. This Llama 2 70B vs Zephyr-7B overview guide and comparison video will provide more information on the development and performance of Zephyr-7B. Exploring its training process, the use of Direct Preference Optimization (DPO) for alignment, and its performance in comparison to other models.  In Greek mythology, Zephyr or Zephyrus is the god of the west wind, often depicted as a gentle breeze bringing in the spring season.

Zephyr-7B-α, the first model in the Zephyr series, is a fine-tuned version of Mistral-7B-v0.1. The model was trained on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO), a technique that has proven to be effective in enhancing the performance of language models. Interestingly, the developers found that removing the in-built alignment of these datasets boosted performance on MT Bench and made the model more helpful. However, this also means that the model is likely to generate problematic text when prompted to do so, and thus, it is recommended for use only for educational and research purposes.

Llama 2 70B vs Zephyr-7B

If you are interested in learning more the Prompt Engineering YouTube channel has created a new video comparing it with  the massive Llama 2 70B AI model.

 Previous articles we have written that you might be interested in on the subject of the Mistral and Llama 2 AI models :

The initial fine-tuning of Zephyr-7B-α was carried out on a variant of the UltraChat dataset. This dataset contains a diverse range of synthetic dialogues generated by ChatGPT, providing a rich and varied source of data for training. The model was then further aligned with TRL’s DPOTrainer on the openbmb/UltraFeedback dataset, which contains 64k prompts and model completions that are ranked by GPT-4.

It’s important to note that Zephyr-7B-α has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT. This means that the model can produce problematic outputs, especially when prompted to do so. The size and composition of the corpus used to train the base model (mistralai/Mistral-7B-v0.1) are unknown, but it is likely to have included a mix of Web data and technical sources like books and code.

When it comes to performance, Zephyr-7B-α holds its own against other models. A comparison with the Lama 270 billion model, for instance, shows that Zephyr’s development and training process has resulted in a model that is capable of producing high-quality outputs. However, as with any AI model, the quality of the output is largely dependent on the quality and diversity of the input data.

Testing of Zephyr’s writing, reasoning, and coding abilities has shown promising results. The model is capable of generating coherent and contextually relevant text, demonstrating a level of understanding and reasoning that is impressive for a language model. Its coding abilities, while not on par with a human coder, are sufficient for basic tasks and provide a glimpse into the potential of AI in the field of programming.

The development and performance of the Zephyr-7B-α AI model represent a significant step forward in the field of AI language models. Its training process, use of DPO for alignment, and performance in comparison to other models all point to a future where AI models like Zephyr could play a crucial role in various fields, from education and research to programming and beyond. However, it’s important to remember that Zephyr, like all AI models, is a tool and its effectiveness and safety depend on how it is used and managed.

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How to deploy a Llama 2 70B API in just 5 clicks

How to deploy a Llama 2 70B API in just 5 clicks

Trelis Research has recently released a comprehensive guide on how to set up an API for the Llama 70B using RunPod, a cloud computing platform primarily designed for AI and machine learning applications. This guide provides a step-by-step process on how to optimize the performance of the Llama 70B API using RunPod’s key offerings, including GPU Instances, Serverless GPUs, and AI Endpoints.

RunPod’s GPU Instances allow users to deploy container-based GPU instances that spin up in seconds using both public and private repositories. These instances are available in two different types: Secure Cloud and Community Cloud. The Secure Cloud operates in T3/T4 data centers, ensuring high reliability and security, while the Community Cloud connects individual compute providers to consumers through a vetted, secure peer-to-peer system.

The Serverless GPU service, part of RunPod’s Secure Cloud offering, provides pay-per-second serverless GPU computing, bringing autoscaling to your production environment. This service guarantees low cold-start times and stringent security measures. AI Endpoints, on the other hand, are fully managed and scaled to handle any workload. They are designed for a variety of applications including Dreambooth, Stable Diffusion, Whisper, and more.

Deploying a Llama 2 70B API on RunPod

To automate workflows and manage compute jobs effectively, RunPod provides a CLI / GraphQL API. Users can access multiple points for coding, optimizing, and running AI/ML jobs, including SSH, TCP Ports, and HTTP Ports. RunPod also offers OnDemand and Spot GPUs to suit different compute needs, and Persistent Volumes to ensure the safety of your data even when your pods are stopped. The Cloud Sync feature allows seamless data transfer to any cloud storage.

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Setting up RunPod

 

To set up an API for Llama 70B, users first need to create an account on RunPod. After logging in, users should navigate to the Secure Cloud section and choose a pricing structure that suits their needs. Users can then deploy a template and find a Trellis Research Lab Llama 2 70B. Once the model is loaded, the API endpoint will be ready for use.

To increase the inference speed, users can run multiple GPUs in parallel. Users can also run a long context model by searching for a different template by trellis research. The inference software allows users to make multiple requests to the API at the same time. Sending in large batches can make the approach as economic as using the open AIA API. Larger GPUs are needed for more batches or longer context length.

One of the key use cases for doing inference on a GPU is for data preparation. Users can also run their own model by swapping out the model name on hugging face. Access to the Llama 2 Enterprise Installation and Inference Guide server setup repo can be purchased for €49.99 for more detailed information on setting up a server and maximizing throughput for models.

Deploying a Meta’s Llama 2 70B API using RunPod is a straightforward process that can be accomplished in just a few steps. With the right tools and guidance, users can optimize the performance of their API and achieve their AI and machine learning objectives.

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