Researchers create a low-cost open-source ai model to analyse how openai’s o1 reasons

Researchers create a low-cost open-source ai model to analyse how openai’s o1 reasons

Researchers from Stanford University and Washington University have developed an open-source artificial intelligence (ai) model that is comformable in performance to openai. The main objective of the resultars was not to create a powerful reasoning-focused model but to undersrstand how the San Francisco-Based Ai Firm Instructed Its O1 Series Models to Perform Test Test Test Time to SCALING.

Notable, The Researchers were able to showcase the methodology and replicate the model’s behaviour Archers have detailed the methodology and process of development the model in a study published in the Pre-Print Journal Arxiv. The process involved creating a synthetic dataset from a different ai model and using seveal new techniques such and supervised fin-tuning (SFT). The model is available in a github listing.it should be noted that ai model was not built from scratch. The developers used the qwen2.5-32b-insstruct and distilled it to create the S1-32B Large Language Model (LLM). Released in September 2024, The Model is Capable But Given Its Size and Lack of Reasoning Capability, it cannot match up to openai’s o1.during the process, the resarchers may Face (api) to generate reasoning traces and responses . A total of 59,000 triplets of questions, reasoning traces (the chain of thought or cot), and responses were extracted from the API. A dataset called the S1k was then created by selecting e-tuning on the qwen2.5-32b -Instruct model. For this, basic fin-tuning hyperparameters ware. The distillation process took 26 minutes of training on 16 nvidia h100 gpus.til this point, The Researchers Had No Idea How Openai Trained The Models to “Think” and how it managed to stop the following proyses. Without this, a model runs the resk of overthinking indefinite as it second-guaseses it is output wasting valuable processing power.While fin-tuning the model, the Researcher finger They found that they could manipulate the infection time by adding and xml tags. Once a model reactions the end tag, it is told to change its voice to an authoritative tone for the final answer. Notably, Infererance Time is the Near Real-Time Responses that a Typical AI Model Generates. Anything more than this would require careful manipulation of the code. Once added, The Model Began Second-Guesing and Verifying Its Output. Then, the tag was used to either shorten this test time scaling phase or lengthen it.then, the resarchers also experience with several other phrases such as “Alternatively”, and “Hmmmm” Etrics were achieved when using The “wait” tag. By bringing the model close to the performance of o1, the results of the might be the method used by openai to fin-tune its -32B AI Model Under $ 50 (roughly Rs. 4,380), highlighting that creating a post-training structure for reasoning models can be done at an extramely low cost. 6

Credit-Read More

// Function to fetch the latest posts
function fetchLatestPosts() {
const feedUrl = ‘https://newskiosk.pro/feed/’; // Replace with your blog’s RSS feed URL
fetch(feedUrl)
.then(response => response.text())
.then(str => new window.DOMParser().parseFromString(str, “text/xml”))
.then(data => {
const items = Array.from(data.querySelectorAll(“item”));
const latestPostsContainer = document.getElementById(“latest-posts”);
latestPostsContainer.innerHTML = ”; // Clear previous posts

// Shuffle the items array
const shuffledItems = items.sort(() => Math.random() – 0.5);

// Select the first 5 items from the shuffled array
const selectedItems = shuffledItems.slice(0, 5);

// Loop through the selected items and display them
selectedItems.forEach(post => {
const link = post.querySelector(“link”).textContent;
const description = post.querySelector(“description”).textContent;

// Create a new post element
const postElement = document.createElement(“div”);
postElement.classList.add(“latest-post”);
postElement.innerHTML = `

${description} Read more

`;

// Append the new post element to the container
latestPostsContainer.appendChild(postElement);
});
})
.catch(error => console.error(‘Error fetching the latest posts:’, error));
}

// Call the function to fetch and display the latest posts
fetchLatestPosts();