First-Ever General AI Agent, Manus. But it’s restricted by the invite code and money. However, we haven’t got the prices yet. But it’s not gonna be free. So, what we do now. Well, let’s move to our saviours, the open-source community
Well, guess what? OpenManus is like the answer to your prayers! It’s basically a free version of Manus that you can just download and use right now. It does all that cool AI agent stuff like figuring things out on its own, working with other programs, and automating tasks. And the best part? You don’t have to wait in line or pay anything, and you can see exactly how it’s built. Pretty awesome, huh?
OpenManus is an open-source project designed to allow users to create and utilize their own AI agents without requiring an invite code, unlike the proprietary Manus platform. It’s developed by a team including members from MetaGPT and aims to democratize access to AI agent creation.
Key Features
No Invite Code Required: Unlike Manus, OpenManus eliminates the need for an invite code, making it accessible to everyone.
Open-Source Implementation: The project is fully open-source, encouraging community contributions and improvements.
Integration with OpenManus-RL: Collaborates with researchers from UIUC on reinforcement learning tuning methods for LLM agents.
Active Development: The team is actively working on enhancements including improved planning capabilities, standardized evaluation metrics, model adaptation, containerized deployment, and expanded example libraries.
git clone https://github.com/mannaandpoem/OpenManus.git
cd OpenManus
Install dependencies:
pip install -r requirements.txt
Method 2: Using uv (Recommended)
Install uv:
curl -LsSf https://astral.sh/uv/install.sh | sh
Clone the repository:
git clone https://github.com/mannaandpoem/OpenManus.git
cd OpenManus
Create and activate a virtual environment:
uv venv
source .venv/bin/activate # On Unix/macOS
# Or on Windows:
# .venv\Scripts\activate
Install dependencies:
uv pip install -r requirements.txt
Configuration:
Create a config.toml file in the config directory by copying the example:cp config/config.example.toml config/config.toml
Edit config/config.toml to add your API keys and customize settings:
# Global LLM configuration
[llm]
model = "gpt-4o"
base_url = "https://api.openai.com/v1"
api_key = "sk-..." # Replace with your actual API key
max_tokens = 4096
temperature = 0.0
# Optional configuration for specific LLM models
[llm.vision]
model = "gpt-4o"
base_url = "https://api.openai.com/v1"
api_key = "sk-..." # Replace with your actual API key
Running OpenManus
After completing the installation and configuration steps, you can run OpenManus with a single command. The specific command may vary depending on your setup, but generally, you can execute:python main.py
Then input your idea via the terminal when prompted.
For the unstable version, you might need to use a different command as specified in the project documentation.
Infinite Retrieval is a method to enhance LLMs Attention in Long-Context Processing.” The core problem it solves is that traditional LLMs, like those based on the Transformer architecture, struggle with long contexts because their attention mechanisms scale quadratically with input length. Double the input, and you’re looking at four times the memory and compute—yikes! This caps how much text they can process at once, usually to something like 32K tokens or less, depending on the model.
The folks behind this (Xiaoju Ye, Zhichun Wang, and Jingyuan Wang) came up with a method called InfiniRetri. InfiniRetri is a trick that helps computers quickly find the important stuff in a giant pile of words, like spotting a treasure in a huge toy box, without looking at everything.
It’s a clever twist that lets LLMs handle “infinite” context lengths—think millions of tokens—without needing extra training or external tools like Retrieval-Augmented Generation (RAG). Instead, it uses the model’s own attention mechanism in a new way to retrieve relevant info from absurdly long inputs. The key insight? They noticed a link between how attention is distributed across layers and the model’s ability to fetch useful info, so they leaned into that to make retrieval smarter and more efficient.
Here’s what makes it tick:
Attention Allocation Trick: InfiniRetri piggybacks on the LLM’s existing attention info (you know, those key, value, and query vectors) to figure out what’s worth retrieving from a massive input. No need for separate embeddings or external databases.
No Training Needed: It’s plug-and-play—works with any Transformer-based LLM right out of the box, which is huge for practicality.
Performance Boost: Tests show it nails tasks like the Needle-In-a-Haystack (NIH) test with 100% accuracy over 1M tokens using a tiny 0.5B parameter model. It even beats bigger models, cuts inference latency, and computes overhead by a ton—up to a 288% improvement on real-world benchmarks.
In short, it’s like giving your LLM a superpower to sift through a haystack the size of a planet and still find that one needle, all while keeping things fast and lean.
What’s This “Infinite Retrieval” Thing?
Imagine you’ve got a huge toy box—way bigger than your room. It’s stuffed with millions of toys: cars, dolls, blocks, even some random stuff like a sock or a candy wrapper. Now, I say, “Find me the tiny red racecar!” You can’t look at every single toy because it’d take forever, right? Your arms would get tired, and you’d probably give up.
Regular language models (those smart computer brains we call LLMs) are like that. When you give them a giant story or a massive pile of words (like a million toys), they get confused. They can only look at a small part of the pile at once—like peeking into one corner of your toy box. If the red racecar is buried deep somewhere else, they miss it.
Infinite Retrieval is like giving the computer a magic trick. It doesn’t have to dig through everything. Instead, it uses a special “attention” superpower to quickly spot the red racecar, even in that giant toy box, without making a mess or taking all day.
How Does It Work?
Let’s pretend the computer is your friend, Robo-Bob. Robo-Bob has these cool glasses that glow when he looks at stuff that matters. Here’s what happens:
Big Pile of Words: You give Robo-Bob a super long story—like a book that’s a mile long—about a dog, a cat, a pirate, and a million other things. You ask, “What did the pirate say to the dog?”
Magic Glasses: Robo-Bob doesn’t read the whole mile-long book. His glasses light up when he sees important words—like “pirate” and “dog.” He skips the boring parts about the cat chasing yarn or the wind blowing.
Quick Grab: Using those glowing clues, he zooms in, finds the pirate saying, “Arf, matey!” to the dog, and tells you. It’s fast—like finding that red racecar in two seconds instead of two hours!
The trick is in those glasses (called “attention” in computer talk). They help Robo-Bob know what’s important without looking at every single toy or word.
Real-Time Example: Finding Your Lost Sock
Imagine you lost your favorite striped sock at school. Your teacher dumps a giant laundry basket with everyone’s clothes in front of you—hundreds of shirts, pants, and socks! A normal computer would check every single shirt and sock one by one—super slow. But with Infinite Retrieval, it’s like the computer gets a sock-sniffing dog. The dog smells your sock’s stripes from far away, ignores the shirts and pants, and runs straight to it. Boom—sock found in a snap!
In real life, this could help with:
Reading Long Books Fast: Imagine a kid asking, “What’s the treasure in this 1,000-page pirate story?” The computer finds it without reading every page.
Searching Big Videos: You ask, “What did the superhero say at the end of this 10-hour movie?” It skips to the end and tells you, “I’ll save the day!”
Why’s It Awesome?
It’s fast—like finding your sock before recess ends.
It works with tiny robots, not just big ones. Even a little computer can do it!
It doesn’t need extra lessons. Robo-Bob already knows the trick when you build him.
So, buddy, it’s like giving a computer a treasure map and a flashlight to find the good stuff in a giant pile—without breaking a sweat! Did that make sense? Want me to explain any part again with more toys or games?
Large Language Models (LLMs) have revolutionized the field of Artificial Intelligence, powering applications from chatbots to content generation. At the heart of these powerful models lie LLM parameters, numerical values that dictate how an LLM learns and processes information. This comprehensive guide will delve into what LLM parameters are, their significance in model performance, and how they influence various aspects of AI development.
We’ll explore this topic in a way that’s accessible to both beginners and those with a more technical background.
How LLM Parameters Impact Performance
The number of LLM parameters directly correlates with the model’s capacity to understand and generate human-like text. Models with more parameters can typically handle more complex tasks, exhibit better reasoning abilities, and produce more coherent and contextually relevant outputs.
However, a larger parameter count doesn’t always guarantee superior performance. Other factors, such as the quality of the training data and the architecture of the model, also play crucial roles.
Parameters as the Model’s Knowledge and Capacity
In the realm of deep learning, and specifically for LLMs built upon neural network architectures (often Transformers), parameters are the adjustable, learnable variables within the model. Think of them as the fundamental building blocks that dictate the model’s behavior and capacity to learn complex patterns from data.
Neural Networks and Connections: LLMs are structured as interconnected layers of artificial neurons. These neurons are connected by pathways, and each connection has an associated weight. These weights, along with biases (another type of parameter), are what we collectively refer to as “parameters.”
Learning Through Parameter Adjustment: During the training process, the LLM is exposed to massive datasets of text and code. The model’s task is to predict the next word in a sequence, or perform other language-related objectives. To achieve this, the model iteratively adjusts its parameters (weights and biases) based on the errors it makes. This process is guided by optimization algorithms and aims to minimize the difference between the model’s predictions and the actual data.
Parameters as Encoded Knowledge: As the model trains and parameters are refined, these parameters effectively encode the patterns, relationships, and statistical regularities present in the training data. The parameters become a compressed representation of the knowledge the model acquires about language, grammar, facts, and even reasoning patterns.
More Parameters = Higher Model Capacity: The number of parameters directly relates to the model’s capacity. A model with more parameters has a greater ability to:
Store and represent more complex patterns. Imagine a larger canvas for a painter – more parameters offer more “space” to capture intricate details of language.
Learn from larger and more diverse datasets. A model with higher capacity can absorb and generalize from more information.
Potentially achieve higher accuracy and perform more sophisticated tasks. More parameters can lead to better performance, but it’s not the only factor (architecture, training data quality, etc., also matter significantly).
Analogy Time: The Grand Library of Alexandria
Parameters as Bookshelves and Connections: Imagine the parameters of an LLM are like the bookshelves in the Library of Alexandria and the organizational system connecting them.
Number of Parameters (Model Size) = Number of Bookshelves and Complexity of Organization: A library with more bookshelves (more parameters) can hold more books (more knowledge). Furthermore, a more complex and well-organized system of indexing, cross-referencing, and connecting those bookshelves (more intricate parameter relationships) allows for more sophisticated knowledge retrieval and utilization.
Training Data = The Books in the Library: The massive text datasets used to train LLMs are like the vast collection of scrolls and books in the Library of Alexandria.
Learning = Organizing and Indexing the Books: The training process is analogous to librarians meticulously organizing, cataloging, and cross-referencing all the books. They establish a system (the parameter settings) that allows anyone to efficiently find information, understand relationships between different topics, and even generate new knowledge based on existing works.
A Small Library (Fewer Parameters): A small local library with limited bookshelves can only hold a limited collection. Its knowledge is restricted, and its ability to answer complex queries or generate new insightful content is limited.
The Grand Library (Many Parameters): The Library of Alexandria, with its legendary collection, could offer a far wider range of knowledge, support complex research, and inspire new discoveries. Similarly, an LLM with billions or trillions of parameters has a vast “knowledge base” and the potential for more complex and nuanced language processing.
The Twist: Quantization and Model Weights Size
While the number of parameters is the primary indicator of model size and capacity, the actual file size of the model weights on disk is also affected by quantization.
Data Types and Precision: Parameters are stored as numerical values. The data type used to represent these numbers determines the precision and the storage space required. Common data types include:
float32 (FP32): Single-precision floating-point (4 bytes per parameter). Offers high precision but larger size.
float16 (FP16, half-precision): Half-precision floating-point (2 bytes per parameter). Reduces size and can speed up computation, with a slight trade-off in precision.
bfloat16 (Brain Float 16): Another 16-bit format (2 bytes per parameter), designed for machine learning.
int8 (8-bit integer): Integer quantization (1 byte per parameter). Significant size reduction, but more potential accuracy loss.
int4 (4-bit integer): Further quantization (0.5 bytes per parameter). Dramatic size reduction, but requires careful implementation to minimize accuracy impact.
Quantization as “Data Compression” for Parameters:Quantization is a technique to reduce the precision (and thus size) of the model weights. It’s like “compressing” the numerical representation of each parameter.
Ollama’s 4-bit Quantization Example: As we saw with Ollama’s Llama 2 (7B), using 4-bit quantization (q4) drastically reduces the model weight file size. Instead of ~28GB for a float32 7B model, it becomes around 3-4GB. This is because each parameter is stored using only 4 bits (0.5 bytes) instead of 32 bits (4 bytes).
Trade-offs of Quantization: Quantization is a powerful tool for making models more efficient, but it often involves a trade-off. Lower precision (like 4-bit) can lead to a slight decrease in accuracy compared to higher precision (float32). However, for many applications, the benefits of reduced size and faster inference outweigh this minor performance impact.
Calculating Approximate Model Weights Size
To estimate the model weights file size, you need to know:
Number of Parameters (e.g., 7B, 13B, 70B).
Data Type (Float Precision/Quantization Level).
Formula:
Approximate Size in Bytes = (Number of Parameters) * (Bytes per Parameter for the Data Type)
Approximate Size in GB = (Size in Bytes) / (1024 * 1024 * 1024)
Model Cards (Hugging Face Hub, Model Provider Websites): Look for sections like “Model Details,” “Technical Specs,” “Quantization.” Keywords: dtype, precision, quantized.
Configuration Files (config.json, etc.): Check for torch_dtype or similar keys.
Code Examples/Loading Instructions: See if the code specifies torch_dtype or quantization settings.
Inference Library Documentation: Libraries like transformers often have default data types and ways to check/set precision.
Why Model Size Matters: Practical Implications
Storage Requirements: Larger models require more disk space to store the model weights.
Memory (RAM) Requirements: During inference (using the model), the model weights need to be loaded into memory (RAM). Larger models require more RAM.
Inference Speed: Larger models can sometimes be slower for inference, especially if memory bandwidth becomes a bottleneck. Quantization can help mitigate this.
Accessibility and Deployment: Smaller, quantized models are easier to deploy on resource-constrained devices (laptops, mobile devices, edge devices) and are more accessible to users with limited hardware.
Computational Cost (Training and Inference): Training larger models requires significantly more computational resources (GPUs/TPUs) and time. Inference can also be more computationally intensive.
The “size” of an LLM, as commonly discussed in terms of billions or trillions, primarily refers to the number of parameters. More parameters generally indicate a higher capacity model, capable of learning more complex patterns and potentially achieving better performance. However, the actual file size of the model weights is also heavily influenced by quantization, which reduces the precision of parameter storage to create more efficient models.
Understanding both parameters and quantization is essential for navigating the world of LLMs, making informed choices about model selection, and appreciating the engineering trade-offs involved in building these powerful AI systems. As the field advances, we’ll likely see even more innovations in model architectures and quantization techniques aimed at creating increasingly capable yet efficient LLMs accessible to everyone.
Building AI agents that interact with the web presents unique challenges. One of the most frustrating is the lack of persistent browser session for ai. Imagine an AI assistant that has to log in to a website every time it needs to perform a task. This repetitive process is not only time-consuming but also disrupts the flow of information and can lead to errors. Fortunately, there’s a solution: maintaining persistent browser sessions for your AI agents.
The Problem with Stateless AI Web Interactions
Without a persistent browser session, each interaction with a website is treated as a brand new visit. This means your AI agent loses all previous context, including login credentials, cookies, and browsing history. This “stateless” approach forces the agent to start from scratch each time, leading to:
Repetitive Logins: Constant login prompts hinder automation and slow down processes.
Loss of Context: Crucial information from previous interactions is lost, impacting the agent’s ability to perform complex tasks.
Inefficient Resource Use: Repeatedly loading websites and resources consumes unnecessary time and computing power.
Repetitive Logins: Constant login prompts hinder automation and slow down processes.
Loss of Context: Crucial information from previous interactions is lost, impacting the agent’s ability to perform complex tasks.
Inefficient Resource Use: Repeatedly loading websites and resources consumes unnecessary time and computing power.
The Power of Persistent Browser Sessions for AI
A persistent browser session for ai allows your agent to maintain a continuous connection with a website, preserving its state across multiple interactions. This means:
Eliminate Repetitive Logins: Your AI agent stays logged in, ready to perform tasks without interruption.
Preserve Context: Retain crucial information like cookies, browsing history, and form data for seamless task execution.
Streamline Workflow: Enable complex, multi-step automation without constantly restarting the process. This is crucial for tasks like web scraping, data extraction, and automated testing.
How Browser-Use Enables Persistent Sessions
Browser-Use offers a powerful solution for managing persistent browser context for ai. By leveraging its features, you can easily create and maintain browser sessions, allowing your AI agents to operate with maximum efficiency. This functionality is especially beneficial for long-running ai browser sessions that require continuous interaction with web applications.
Installation Guide
Prerequisites
Python 3.11 or higher
Git (for cloning the repository)
Option 1: Local Installation
Read the quickstart guide or follow the steps below to get started.
Step 1: Clone the Repository
git clone https://github.com/browser-use/web-ui.git
cd web-ui
Step 2: Set Up Python Environment
We recommend using uv for managing the Python environment.
Using uv (recommended):
uv venv --python 3.11
Activate the virtual environment:
Windows (Command Prompt):
.venv\Scripts\activate
Windows (PowerShell):
.\.venv\Scripts\Activate.ps1
macOS/Linux:
source .venv/bin/activate
Step 3: Install Dependencies
Install Python packages:
uv pip install -r requirements.txt
Install Playwright:
playwright install
Step 4: Configure Environment
Create a copy of the example environment file:
Windows (Command Prompt):
copy .env.example .env
macOS/Linux/Windows (PowerShell):
cp .env.example .env
Open .env in your preferred text editor and add your API keys and other settings
git clone https://github.com/browser-use/web-ui.git
cd web-ui
Create and configure environment file:
Windows (Command Prompt):
copy .env.example .env
macOS/Linux/Windows (PowerShell):
cp .env.example .env
Edit .env with your preferred text editor and add your API keys
Run with Docker:
# Build and start the container with default settings (browser closes after AI tasks)
docker compose up --build
# Or run with persistent browser (browser stays open between AI tasks)
CHROME_PERSISTENT_SESSION=true docker compose up --build
Access the Application:
Web Interface: Open http://localhost:7788 in your browser
VNC Viewer (for watching browser interactions): Open http://localhost:6080/vnc.html
Default VNC password: “youvncpassword”
Can be changed by setting VNC_PASSWORD in your .env file
Docker Setup
Environment Variables:
All configuration is done through the .env file
Available environment variables:
# LLM API Keys
OPENAI_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here
GOOGLE_API_KEY=your_key_here
# Browser Settings
CHROME_PERSISTENT_SESSION=true # Set to true to keep browser open between AI tasks
RESOLUTION=1920x1080x24 # Custom resolution format: WIDTHxHEIGHTxDEPTH
RESOLUTION_WIDTH=1920 # Custom width in pixels
RESOLUTION_HEIGHT=1080 # Custom height in pixels
# VNC Settings
VNC_PASSWORD=your_vnc_password # Optional, defaults to "vncpassword"
Platform Support:
Supports both AMD64 and ARM64 architectures
For ARM64 systems (e.g., Apple Silicon Macs), the container will automatically use the appropriate image
Browser Persistence Modes:
Default Mode (CHROME_PERSISTENT_SESSION=false):
Browser opens and closes with each AI task
Clean state for each interaction
Lower resource usage
Persistent Mode (CHROME_PERSISTENT_SESSION=true):
Browser stays open between AI tasks
Maintains history and state
Allows viewing previous AI interactions
Set in .env file or via environment variable when starting container
Viewing Browser Interactions:
Access the noVNC viewer at http://localhost:6080/vnc.html
Enter the VNC password (default: “vncpassword” or what you set in VNC_PASSWORD)
Direct VNC access available on port 5900 (mapped to container port 5901)
You can now see all browser interactions in real-time
Persistent browser sessions are essential for building efficient and robust AI agents that interact with the web. By eliminating repetitive logins, preserving context, and streamlining workflows, you can unlock the true potential of AI web automation. Explore Browser-Use and discover how its persistent session management can revolutionize your AI development process. Start building smarter, more efficient AI agents today!
Best and free platform to deploy Python application
Several platforms offer free options for deploying Python applications, each with its own features and limitations. Here are some of the top contenders:
Render:Render is a cloud service that allows you to build and run apps and websites, with free TLS certificates, a global CDN, and auto-deploys from Git[1]. It supports web apps, static sites, Docker containers, cron jobs, background workers, and fully managed databases. Most services, including Python web apps, have a free tier to get started[1]. Render’s free auto-scaling feature ensures your app has the necessary resources, and everything hosted on Render gets a free TLS certificate. It is a user-friendly Heroku alternative, offering a streamlined deployment process and an intuitive management interface.
PythonAnywhere: This platform has been around for a while and is well-known in the Python community[1]. It is a reliable and simple service to get started with[1]. You get one web app with a pythonanywhere.com domain for free, with upgraded plans starting at $5 per month.
Railway: Railway is a deployment platform where you can provision infrastructure, develop locally, and deploy to the cloud[1]. They provide templates to get started with different frameworks and allow deployment from an existing GitHub repo[1]. The Starter tier can be used for free without a credit card, and the Developer tier is free under $5/month.
GitHub: While you can’t host web apps on GitHub, you can schedule scripts to run regularly with GitHub Actions and cron jobs. The free tier includes 2,000 minutes per month, which is enough to run many scripts multiple times a day.
Anvil: Anvil is a Python web app platform that allows you to build and deploy web apps for free. It offers a drag-and-drop designer, a built-in Python server environment, and a built-in Postgres-backed database.
When choosing a platform, consider the specific needs of your application, including the required resources, dependencies, and traffic volume. Some platforms may have limitations on outbound internet access or the number of projects you can create.
Imagine having a 24/7 health companion that analyzes your medical history, tracks real-time vitals, and offers tailored advice—all while keeping your data private. This is the reality of AI health assistants, open-source tools merging artificial intelligence with healthcare to empower individuals and professionals alike. Let’s dive into how these systems work, their transformative benefits, and how you can build one using platforms like OpenHealthForAll
What Is an AI Health Assistant?
An AI health assistant is a digital tool that leverages machine learning, natural language processing (NLP), and data analytics to provide personalized health insights. For example:
OpenHealth consolidates blood tests, wearable data, and family history into structured formats, enabling GPT-powered conversations about your health.
Aiden, another assistant, uses WhatsApp to deliver habit-building prompts based on anonymized data from Apple Health or Fitbit.
These systems prioritize privacy, often running locally or using encryption to protect sensitive information.
Why AI Health Assistants Matter: 5 Key Benefits
Centralized Health Management Integrate wearables, lab reports, and EHRs into one platform. OpenHealth, for instance, parses blood tests and symptoms into actionable insights using LLMs like Claude or Gemini.
Real-Time Anomaly Detection Projects like Kavya Prabahar’s virtual assistant use RNNs to flag abnormal heart rates or predict fractures from X-rays.
Privacy-First Design Tools like Aiden anonymize data via Evervault and store records on blockchain (e.g., NearestDoctor’s smart contracts) to ensure compliance with regulations like HIPAA.
Empathetic Patient Interaction Assistants like OpenHealth use emotion-aware AI to provide compassionate guidance, reducing anxiety for users managing chronic conditions.
Cost-Effective Scalability Open-source frameworks like Google’s Open Health Stack (OHS) help developers build offline-capable solutions for low-resource regions, accelerating global healthcare access.
Challenges and Ethical Considerations
While promising, AI health assistants face hurdles:
Data Bias: Models trained on limited datasets may misdiagnose underrepresented groups.
Interoperability: Bridging EHR systems (e.g., HL7 FHIR) with AI requires standardization efforts like OHS.
Regulatory Compliance: Solutions must balance innovation with safety, as highlighted in Nature’s call for mandatory feedback loops in AI health tech.
Build Your Own AI Health Assistant: A Developer’s Guide
Step 1: Choose Your Stack
Data Parsing: Use OpenHealth’s Python-based parser (migrating to TypeScript soon) to structure inputs from wearables or lab reports.
AI Models: Integrate LLaMA or GPT-4 via APIs, or run Ollama locally for privacy.
Step 2: Prioritize Security
Encrypt user data with Supabase or Evervault.
Implement blockchain for audit trails, as seen in NearestDoctor’s medical records system.
Step 3: Start the setup
Clone the Repository:
git clone https://github.com/OpenHealthForAll/open-health.git
cd open-health
Setup and Run:
# Copy environment file
cp .env.example .env
# Add API keys to .env file:
# UPSTAGE_API_KEY - For parsing (You can get $10 credit without card registration by signing up at https://www.upstage.ai)
# OPENAI_API_KEY - For enhanced parsing capabilities
# Start the application using Docker Compose
docker compose --env-file .env up
For existing users, use:
docker compose --env-file .env up --build
Access OpenHealth: Open your browser and navigate to http://localhost:3000 to begin using OpenHealth.
The Future of AI Health Assistants
Decentralized AI Marketplaces: Platforms like Ocean Protocol could let users monetize health models securely.
AI-Powered Diagnostics: Google’s Health AI Developer Foundations aim to simplify building diagnostic tools for conditions like diabetes.
Global Accessibility: Initiatives like OHS workshops in Kenya and India are democratizing AI health tech.
Your Next Step
Contribute to OpenHealth’s GitHub repo to enhance its multilingual support.
OmniHuman-1 is an advanced AI model developed by ByteDance that generates realistic human videos from a single image and motion signals, such as audio or video inputs. This model supports various visual and audio styles, accommodating different aspect ratios and body proportions, including portrait, half-body, and full-body formats. Its capabilities extend to producing lifelike videos with natural motion, lighting, and texture details.
As of now, ByteDance has not released the OmniHuman-1 model or its weights to the public. The official project page states, “Currently, we do not offer services or downloads anywhere. Please be cautious of fraudulent information. We will provide timely updates on future developments.”
ByteDance, the parent company of TikTok, has recently unveiled OmniHuman-1, an advanced AI model capable of generating realistic human videos from a single image and motion signals such as audio or video inputs. This development marks a significant leap in AI-driven human animation, offering potential applications across various industries.
Key Features of OmniHuman-1
Multimodal Input Support: OmniHuman-1 can generate human videos based on a single image combined with motion signals, including audio-only, video-only, or a combination of both. This flexibility allows for diverse applications, from creating talking head videos to full-body animations.
Aspect Ratio Versatility: The model supports image inputs of any aspect ratio, whether they are portraits, half-body, or full-body images. This adaptability ensures high-quality results across various scenarios, catering to different content creation needs.
Enhanced Realism: OmniHuman-1 significantly outperforms existing methods by generating extremely realistic human videos based on weak signal inputs, especially audio. The realism is evident in comprehensive aspects, including motion, lighting, and texture details.
Current Availability
As of now, ByteDance has not released the OmniHuman-1 model or its weights to the public. The official project page states, “Currently, we do not offer services or downloads anywhere. Please be cautious of fraudulent information. We will provide timely updates on future developments.”
Implications and Considerations
The capabilities of OmniHuman-1 open up numerous possibilities in fields such as digital content creation, virtual reality, and entertainment. However, the technology also raises ethical considerations, particularly concerning the potential for misuse in creating deepfake content. It is crucial for developers, policymakers, and users to engage in discussions about responsible use and the establishment of guidelines to prevent abuse.
OmniHuman-1 represents a significant advancement in AI-driven human animation, showcasing the rapid progress in this field. While its public release is still pending, the model’s demonstrated capabilities suggest a promising future for AI applications in creating realistic human videos. As with any powerful technology, it is essential to balance innovation with ethical considerations to ensure beneficial outcomes for society.
Virtuoso-Medium-v2 is here, Are you ready to harness the power of Virtuoso-Medium-v2 , the next-generation 32-billion-parameter language model? Whether you’re building advanced chatbots, automating workflows, or diving into research simulations, this guide will walk you through installing and running Virtuoso-Medium-v2 on your local machine. Let’s get started!
Why Choose Virtuoso-Medium-v2?
Before we dive into the installation process, let’s briefly understand why Virtuoso-Medium-v2 stands out:
Distilled from Deepseek-v3 : With over 5 billion tokens worth of logits, it delivers unparalleled performance in technical queries, code generation, and mathematical problem-solving.
Cross-Architecture Compatibility : Thanks to “tokenizer surgery,” it integrates seamlessly with Qwen and Deepseek tokenizers.
Apache-2.0 License : Use it freely for commercial or non-commercial projects.
Now that you know its capabilities, let’s set it up locally.
Prerequisites
Before installing Virtuoso-Medium-v2, ensure your system meets the following requirements:
Hardware :
GPU with at least 24GB VRAM (recommended for optimal performance).
Sufficient disk space (~50GB for model files).
Software :
Python 3.8 or higher.
PyTorch installed (pip install torch).
Hugging Face transformers library (pip install transformers).
Step 1: Download the Model
The first step is to download the Virtuoso-Medium-v2 model from Hugging Face. Open your terminal and run the following commands:
# Install necessary libraries
pip install transformers torch
# Clone the model repository
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "arcee-ai/Virtuoso-Medium-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
This will fetch the model and tokenizer directly from Hugging Face.
Step 2: Prepare Your Environment
Ensure your environment is configured correctly: 1. Set up a virtual environment to avoid dependency conflicts:
python -m venv virtuoso-env
source virtuoso-env/bin/activate # On Windows: virtuoso-env\Scripts\activate
2. Install additional dependencies if needed:
pip install accelerate
Step 3: Run the Model
Once the model is downloaded, you can test it with a simple prompt. Here’s an example script:
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the model and tokenizer
model_name = "arcee-ai/Virtuoso-Medium-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Define your input prompt
prompt = "Explain the concept of quantum entanglement in simple terms."
inputs = tokenizer(prompt, return_tensors="pt")
# Generate output
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Run the script, and you’ll see the model generate a concise explanation of quantum entanglement!
Step 4: Optimize Performance
To maximize performance:
Use quantization techniques to reduce memory usage.
Enable GPU acceleration by setting device_map="auto" during model loading:
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
Troubleshooting Tips
Out of Memory Errors : Reduce the max_new_tokens parameter or use quantized versions of the model.
Slow Inference : Ensure your GPU drivers are updated and CUDA is properly configured.
With Virtuoso-Medium-v2 installed locally, you’re now equipped to build cutting-edge AI applications. Whether you’re developing enterprise tools or exploring STEM education, this model’s advanced reasoning capabilities will elevate your projects.
Ready to take the next step? Experiment with Virtuoso-Medium-v2 today and share your experiences with the community! For more details, visit the official Hugging Face repository .
Exciting news from OpenAI—the highly anticipated o3-mini model is now available in ChatGPT and the API, offering groundbreaking capabilities for a wide range of use cases, particularly in science, math, and coding. First previewed in December 2024, o3-mini is designed to push the boundaries of what small models can achieve while keeping costs low and maintaining the fast response times that users have come to expect from o1-mini.
Key Features of OpenAI o3-mini:
🔹 Next-Level Reasoning for STEM Tasks o3-mini delivers exceptional STEM reasoning performance, with particular strength in science, math, and coding. It maintains the cost efficiency and low latency of its predecessor, o1-mini, but packs a much stronger punch in terms of reasoning power and accuracy.
🔹 Developer-Friendly Features For developers, o3-mini introduces a host of highly-requested features:
Function Calling
Structured Outputs
Developer Messages These features make o3-mini production-ready right out of the gate. Additionally, developers can select from three reasoning effort options—low, medium, and high—allowing for fine-tuned control over performance. Whether you’re prioritizing speed or accuracy, o3-mini has you covered.
🔹 Search Integration for Up-to-Date Answers For the first time, o3-mini works with search, enabling it to provide up-to-date answers along with links to relevant web sources. This integration is part of OpenAI’s ongoing effort to incorporate real-time search across their reasoning models, and while it’s still in an early prototype stage, it’s a step towards an even smarter, more responsive model.
🔹 Enhanced Access for Paid Users Pro, Team, and Plus users will have triple the rate limits compared to o1-mini, with up to 150 messages per day instead of the 50 available on the earlier model. Plus, all paid users can select o3-mini-high, which offers a higher-intelligence version with slightly longer response times, ensuring Pro users have unlimited access to both o3-mini and o3-mini-high.
🔹 Free Users Can Try o3-mini! For the first time, free users can also explore o3-mini in ChatGPT by simply selecting the ‘Reason’ button in the message composer or regenerating a response. This brings access to high-performance reasoning capabilities previously only available to paid users.
🔹 Optimized for Precision & Speed o3-mini is optimized for technical domains, where precision and speed are key. When set to medium reasoning effort, it delivers the same high performance as o1 on complex tasks but with much faster response times. In fact, evaluations show that o3-mini produces clearer, more accurate answers with a 39% reduction in major errors compared to o1-mini.
A Model Built for Technical Excellence
Whether you’re tackling challenging problems in math, coding, or science, o3-mini is designed to give you faster, more precise results. Expert testers have found that o3-mini beats o1-mini in 56% of cases, particularly when it comes to real-world, difficult questions like those found in AIME and GPQA evaluations. It’s a clear choice for tasks that require a blend of intelligence and speed.
Rolling Out to Developers and Users
Starting today, o3-mini is rolling out in the Chat Completions API, Assistants API, and Batch API to developers in API usage tiers 3-5. ChatGPT Plus, Team, and Pro users have access starting now, with Enterprise access coming in February.
This model will replace o1-mini in the model picker, making it the go-to choice for STEM reasoning, logical problem-solving, and coding tasks.
OpenAI o3-mini marks a major leap in small model capabilities—delivering both powerful reasoning and cost-efficiency in one package. As OpenAI continues to refine and optimize these models, o3-mini sets a new standard for fast, intelligent, and reliable solutions for developers and users alike.
Competition Math (AIME 2024)
Mathematics: With low reasoning effort, OpenAI o3-mini achieves comparable performance with OpenAI o1-mini, while with medium effort, o3-mini achieves comparable performance with o1. Meanwhile, with high reasoning effort, o3-mini outperforms both OpenAI o1-mini and OpenAI o1, where the gray shaded regions show the performance of majority vote (consensus) with 64 samples.
PhD-level Science Questions (GPQA Diamond)
PhD-level science: On PhD-level biology, chemistry, and physics questions, with low reasoning effort, OpenAI o3-mini achieves performance above OpenAI o1-mini. With high effort, o3-mini achieves comparable performance with o1.
FrontierMath
Research-level mathematics: OpenAI o3-mini with high reasoning performs better than its predecessor on FrontierMath. On FrontierMath, when prompted to use a Python tool, o3-mini with high reasoning effort solves over 32% of problems on the first attempt, including more than 28% of the challenging (T3) problems. These numbers are provisional, and the chart above shows performance without tools or a calculator.
Competition Code (Codeforces)
Competition coding: On Codeforces competitive programming, OpenAI o3-mini achieves progressively higher Elo scores with increased reasoning effort, all outperforming o1-mini. With medium reasoning effort, it matches o1’s performance.
Software Engineering (SWE-bench Verified)
Software engineering: o3-mini is our highest performing released model on SWEbench-verified. For additional datapoints on SWE-bench Verified results with high reasoning effort, including with the open-source Agentless scaffold (39%) and an internal tools scaffold (61%), see our system card.
LiveBench Coding
LiveBench coding: OpenAI o3-mini surpasses o1-high even at medium reasoning effort, highlighting its efficiency in coding tasks. At high reasoning effort, o3-mini further extends its lead, achieving significantly stronger performance across key metrics.
General knowledge
General knowledge: o3-mini outperforms o1-mini in knowledge evaluations across general knowledge domains.
Model speed and performance
With intelligence comparable to OpenAI o1, OpenAI o3-mini delivers faster performance and improved efficiency. Beyond the STEM evaluations highlighted above, o3-mini demonstrates superior results in additional math and factuality evaluations with medium reasoning effort. In A/B testing, o3-mini delivered responses 24% faster than o1-mini, with an average response time of 7.7 seconds compared to 10.16 seconds.
The AI arms race just saw an unexpected twist. In a world dominated by tech giants like OpenAI, DeepMind, and Meta, a small Chinese AI startup, DeepSeek, has managed to turn heads with a $6 million AI model, the DeepSeek R1. The model has taken the world by surprise by outperforming some of the biggest names in AI, prompting waves of discussions across the industry.
For context, when Sam Altman, the CEO of OpenAI, was asked in 2023 about the possibility of small teams building substantial AI models with limited budgets, he confidently declared that it was “totally hopeless.” At the time, it seemed that only the tech giants, with their massive budgets and computational power, stood a chance in the AI race.
Yet, the rise of DeepSeek challenges that very notion. Despite their modest training budget of just $6 million, DeepSeek has not only competed but outperformed several well-established AI models. This has sparked a serious conversation in the AI community, with experts and entrepreneurs weighing in on how fast the AI landscape is shifting. Many have pointed out that AI is no longer just a game for the tech titans but an open field where small, agile startups can compete.
In the midst of this, a new player has entered the ring: Qwen2.5-Max by Alibaba.
What is Qwen2.5-Max?
Qwen2.5-Max is Alibaba’s latest AI model, and it is already making waves for its powerful capabilities and features. While DeepSeek R1 surprised the industry with its efficiency and cost-effectiveness, Qwen2.5-Max brings to the table a combination of speed, accuracy, and versatility that could very well make it one of the most competitive models to date.
Key Features of Qwen2.5-Max:
Code Execution & Debugging in Real-Time Qwen2.5-Max doesn’t just generate code—it runs and debugs it instantly. This is crucial for developers who need to quickly test and refine their code, cutting down development time.
Ultra-Precise Image Generation Forget about the generic AI-generated art we’ve seen before. Qwen2.5-Max creates highly detailed, instruction-following images that will have significant implications in creative industries ranging from design to film production.
AI Video Generation at Lightning Speed Unlike most AI video tools that take time to generate content, Qwen2.5-Max delivers video outputs much faster than the competition, pushing the boundaries of what’s possible in multimedia creation.
Real-Time Web Search & Knowledge Synthesis One of the standout features of Qwen2.5-Max is its ability to perform real-time web searches, gather data, and synthesize information into comprehensive findings. This is a game-changer for researchers, analysts, and businesses needing quick insights from the internet.
Vision Capabilities for PDFs, Images, and Documents By supporting document analysis, Qwen2.5-Max can extract valuable insights from PDFs, images, and other documents, making it an ideal tool for businesses dealing with a lot of paperwork and data extraction.
DeepSeek vs. Qwen2.5-Max: The New AI Rivalry
With the emergence of DeepSeek’s R1 and Alibaba’s Qwen2.5-Max, the landscape of AI development is clearly shifting. The traditional notion that AI innovation requires billion-dollar budgets is being dismantled as smaller players bring forward cutting-edge technologies at a fraction of the cost.
Sam Altman, CEO of OpenAI, acknowledged DeepSeek’s prowess in a tweet, highlighting how DeepSeek’s R1 is impressive for the price point, but he also made it clear that OpenAI plans to “deliver much better models.” Still, Altman admitted that the entry of new competitors is an invigorating challenge.
But as we know, competition breeds innovation, and this could be the spark that leads to even more breakthroughs in the AI space.
Will Qwen2.5-Max Surpass DeepSeek’s Impact?
While DeepSeek has proven that a small startup can still have a major impact on the AI field, Qwen2.5-Max takes it a step further by bringing real-time functionalities and next-gen creative capabilities to the table. Given Alibaba’s vast resources, Qwen2.5-Max is poised to compete directly with the big players like OpenAI, Google DeepMind, and others.
What makes Qwen2.5-Max particularly interesting is its ability to handle diverse tasks, from debugging code to generating ultra-detailed images and videos at lightning speed. In a world where efficiency is king, Qwen2.5-Max seems to have the upper hand in the race for the most versatile AI model.
The Future of AI: Open-Source or Closed Ecosystems?
The rise of these new AI models also raises an important question about the future of AI development. As more startups enter the AI space, the debate around centralized vs. open-source models grows. Some believe that DeepSeek’s success would have happened sooner if OpenAI had embraced a more open-source approach. Others argue that Qwen2.5-Max could be a sign that the future of AI development is shifting away from being controlled by a few dominant players.
One thing is clear: the competition between AI models like DeepSeek and Qwen2.5-Max is going to drive innovation forward, and we are about to witness an exciting chapter in the evolution of artificial intelligence.
Stay tuned—the AI revolution is just getting started.
