3d Capybara Model

[Jeremias Resendez]

We leverage our novel data synthesis technique called Amplify-instruct (Paper coming soon), the seed distribution and synthesis method are comprised of a synergistic combination of top performing existing data synthesis techniques and distributions used for SOTA models such as Airoboros, Evol-Instruct(WizardLM), Orca, Vicuna, Know_Logic, Lamini, FLASK and others, all into one lean holistically formed methodology for the dataset and model. The seed instructions used for the start of synthesized conversations are largely based on highly regarded datasets like Airoboros, Know logic, EverythingLM, GPTeacher and even entirely new seed instructions derived from posts on the website LessWrong, as well as being supplemented with certain in-house multi-turn datasets like Dove(A successor to Puffin).

While performing great in it's current state, the current dataset used for fine-tuning is entirely contained within 20K training examples, this is 10 times smaller than many similar performing current models, this is signficant when it comes to scaling implications for our next generation of models once we scale our novel syntheiss methods to significantly more examples.

This model was fine-tuned by Nous Research as part of the Capybara/Amplify-Instruct project led by Luigi D.(LDJ) (Paper coming soon), as well as significant dataset formation contributions by J-Supha and general compute and experimentation management by Jeffrey Q. during ablations.

Special thank you to A16Z for sponsoring our training, as well as Yield Protocol for their support in financially sponsoring resources during the R&D of this project.

While most of the tokens within Capybara are newly synthsized and part of datasets like Puffin/Dove, we would like to credit the single-turn datasets we leveraged as seeds that are used to generate the multi-turn data as part of the Amplify-Instruct synthesis.

Includes a portion of conversational data synthesized from less wrong posts, discussing very in-depth details and philosophies about the nature of reality, reasoning, rationality, self-improvement and related concepts.

We leveraged minhash to check for 100%, 99%, 98% and 97% similarity matches between our data and the questions and answers in benchmarks, we found no exact matches, nor did we find any matches down to the 97% similarity level.

You should not test your validations with a feature/acceptance test, it should be with a model test. Then for each form you could test an error is raised if something is invalid instead of testing every error through acceptance tests. For each model it should be something like so:

This post is the fruit of boredom due to the quarantine, some cardboard I had laying around, and a little bit of curiosity. If you want to build a cardboard capybara just like this one, you can download the instructions at the end of this article. If you want to get acquainted with the Slicer for Fusion360 software, or are just curious about how I projected the sculpture, I strongly suggest reading the whole text.

First of all, to make the project I needed to find a good 3D model of a capybara. It was not hard to find this low poly capybara model on Thingverse. Then I needed to download the software I would be using. Autodesk developed it with the intent of it being used in conjunction with Fusion360, but here I used the stand-alone version.

The Slicer for Fusion360 is a pretty straightforward piece of software. You first import the model and then inform the program about the manufacturing settings you are going to use. In this part, it was necessary to measure the thickness of the cardboard I had with some calipers. Then, you select the way you want the program to cut your model.

Slicer for Fusion360 is generally used with a laser cutter. I did not have one. So I simply printed the shape of the pieces into A4 paper. Then I used the prints as molds to cut the cardboard by hand. Not necessary to mention that this is the most time consuming (and patience consuming) part of the whole process. You can see the result of all the cutting bellow.

Capybara is a fast and small footprint software that provides efficient functions for demeaning variables before conducting a GLM estimation via Iteratively Weighted Least Squares (IWLS). This technique is particularly useful when estimating linear models with multiple group fixed effects.

The software can estimate GLMs from the Exponential Family and also Negative Binomial models but the focus will be the Poisson estimator because it is the one used for structural counterfactual analysis in International Trade. It is relevant to add that the IWLS estimator is equivalent with the PPML estimator from Santos-Silva et al. 2006

Tradition QR estimation can be unfeasible due to additional memory requirements. The method, which is based on Halperin 1962 article on vector projections offers important time and memory savings without compromising numerical stability in the estimation process.

The software heavily borrows from Gaure 20213 and Stammann 2018 works on the OLS and IWLS estimator with large k-way fixed effects (i.e., the Lfe and Alpaca packages). The differences are that Capybara uses an elementary approach and uses a minimal C++ code without parallelization, which achieves very good results considering its simplicity. I hope it is east to maintain.

As users become more familiar with AI language models, the limitations of the models can become increasingly apparent and frustrating, leading to a desire for more intelligent and controllable AI interactions. Beam, a groundbreaking chat modality in big-AGI, addresses this need by enabling users to easily engage multiple models simultaneously, fuse their best responses, and achieve better output, wiser decisions with lower risk and reduced AI hallucinations.

Recently, the "More Agents Is All You Need" paper has measured model performance improvements when running multiple instances of the same model. Similarly to Beam, both techniques involve a sampling and a voting phase, however Beam goes further by using AI to analyze and fuse the best part of each answer, and by using diverse model families altogether, which provides stronger diversification benefits.

Beam shows that it is possible to exceed today's level of performance of GPT models, and its design will likely allow to always stay ahead. Beam is designed with principles of human guidance for system stability, parallelism for time-saving, dynamic interfaces for bird's-eye-view decision-making, and orthogonality to maximize the strengths of diverse LLM families, effectively bringing "The Wisdom of Crowds" to the LLM world.

We illustrate the technology with a special kind of text generation: Code. Language models text outputs tend to be verbose, too persuasive, and too singular and time consuming to compare in an article. Thus, we requested HTML/CSS code, which makes it for an easier at-a-glance evaluation. A short video example here.

To illustrate the power of beaming, let's consider a code example. We asked different LLM families: OpenAI, Anthropic, Google and Mistral, to "Make a cool looking capybara shape animation using css in an html file".

The first phase of Beam is a UX feature that allows the user to quickly gather a diverse set of relevant responses without any intelligent analysis, yet. In this initial phase, the user can easily probe multiple models independently, as many times as they want, to explore the solution space and narrow it down to a set of relevant options.

Beam's side-by-side comparison already allows for quick evaluation of each model's output, whether it's a legal document, code or a story, saving time and effort in identifying the most promising starting points for further development.

The power of Beam lies in its ability to fuse the disparate responses from multiple LLM into a cohesive, optimized answer that leverages the best of each. This is where the second phase of Beam comes into play, Fusions. In the Fusions phase, Beam uses LLMs to analyze the generated responses, identify their key components, and intelligently combine them to create a unified, superior answer. Fusion enables:

In this example, both the Claude 3 Opus (left) and OpenAI GPT-4 Turbo (0125) models have successfully synthesized a more visually appealing and coherent animation than four starting AI-generated options. The merged animations are more visually appealing and closely resemble the desired user outcome.

When more is at stake, such as when writing a legal document, planning a vacation, or conceiving a complex software architecture, you now have the ability to harness the collective intelligence, leveraging the proverbial Wisdom of Crowds.

Beam enables users to achieve next-gen LLM performance with today's models. By leveraging diverse LLMs and advanced merging techniques, Beam gets you to better answers faster. Whether you're brainstorming, researching, making decisions, or coding, Beam enhances your workflow and takes your results to the next level.

Beam is available today: explore diverse model combinations, master the merges, and refine your approach based on insights gained. With Beam as your copilot, you'll push beyond what's possible with today's AI models with speed, ease and precision, arriving at the best possible answers.

The Mojo Capybara model is sculpted in a walking position looking ahead. It is painted predominantly brown with black shading on its feet and small ears, and highly detailed with furry texturing all over its body. This South American mammal is a great addition to your wild animal collection.

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