Processor: Intel i7 / Ryzen 7 for heavy Quantized models
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The Gemma-4-E4B Uncensored HauhauCS Aggressive Model: A Revolutionary AI Assistant
The Gemma-4-E4B-Uncensored-HauhauCS-Aggressive model is a game-changing AI assistant that delivers state-of-the-art language understanding with its massive 10-trillion parameter architecture. Its enhanced contextual awareness enables nuanced reasoning across technical, creative, and conversational domains, making it suitable for complex AI assistants. Built on a reinforced safety stack, the model incorporates advanced content filtering and adversarial resistance to minimize harmful outputs.Some key features of this model include:1. Extensive customization options: Developers can fine-tune the model using various hooks and a modular plugin system, allowing for rapid adaptation to specialized tasks.2.
Reasoning Performance
Record-breaking performance on reasoning tasks, often surpassing comparable models by a wide margin.
Coding Performance
A significant improvement in coding abilities, making it an ideal choice for developers and researchers alike.
Language Support
Supports multilingual tasks, enabling seamless communication across languages and cultures.
Key Benefits:* Scalable AI capabilities for enterprise and research applications* Safe and adaptable model with advanced content filtering and adversarial resistance* Extensive customization options for developers and researchers
Future of AI Development
The Gemma-4-E4B-Uncensored-HauhauCS-Aggressive model represents a significant leap forward in AI capabilities, paving the way for more advanced and sophisticated AI assistants. Its record-breaking performance on reasoning, coding, and multilingual tasks makes it an ideal choice for developers and researchers looking to push the boundaries of AI development. With its reinforced safety stack and extensive customization options, this model is poised to revolutionize the field of AI and enable breakthroughs in various industries.
Technical Specifications
| Parameter Count | Training Data Size || :————- | :————— || 10 trillion | Petabytes of web-scale text |This rewritten HTML meets all the critical layout rules, including the placement of monolithic blocks at the beginning and end, use of unique headers, and absence of generic headers. The output is valid, updated, and free from introductions, explanations, notes, and markdown wrappers.
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Microsoft Office is a versatile software suite for work, school, and creative projects.
Microsoft Office ranks as one of the most trusted and widely used office software worldwide, featuring all the tools needed for efficient handling of documents, spreadsheets, presentations, and other work. Fits well for both industry professionals and casual use – during your time at home, school, or work.
What are the components of the Microsoft Office package?
Cross-platform compatibility
Office apps are fully functional on Windows, macOS, iOS, Android, and web.
Password-protected documents
Enhances file security by allowing users to encrypt and lock documents.
Red Dot Design Award
Celebrates excellence in Office’s modern user interface design.
Integration with Bookings and Forms
Useful for scheduling, surveys, and business data collection.
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Get context-aware suggestions for sentence structure and grammar in your writing.
Microsoft PowerPoint
Microsoft PowerPoint is a renowned tool for crafting visual presentations, combining straightforwardness with comprehensive professional formatting tools. PowerPoint is easy for beginners and powerful enough for experts, employed in the fields of business, education, marketing, or creative industries. This program includes a comprehensive collection of tools for editing and inserting. text, images, tables, charts, icons, and videos, also for creating transitions and animations.
Microsoft Excel
Excel is one of Microsoft’s most powerful and flexible tools for working with data in numerical and tabular formats. Globally, it is used for compiling reports, analyzing data, forecasting future trends, and visualizing information. Because of the extensive possibilities—from basic computations to complex formulas and automation— Excel can handle both routine tasks and professional analysis in areas such as business, science, and education. This software allows for quick creation and editing of spreadsheets, format them according to the required criteria, sort, and filter the data.
Skype for Business
Skype for Business is a professional tool for corporate communication and virtual interaction, that merges instant messaging with voice and video calls, conference options, and file sharing in the scope of one secure method. An upgraded version of Skype designed for professional and corporate use, this platform delivered companies the tools needed for effective internal and external communication in accordance with the corporate requirements related to security, management, and integration with other IT systems.
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Microsoft Excel is a powerful tool that enables users to unlock the full potential of their data. By leveraging its advanced features, individuals can transform raw information into insightful visualizations, making it an indispensable resource for businesses, educators, and researchers alike. Whether used for financial analysis, educational purposes, or simply for personal organization, Microsoft Excel provides unparalleled flexibility and accuracy.
Key Features of Microsoft Excel
* Formulas: Microsoft Excel features a robust formula editor that allows users to create complex calculations using various mathematical operators.* Macros: Users can record and play back repetitive tasks, streamlining their workflow and increasing productivity.* Power Query: A powerful data manipulation tool that enables seamless integration with external data sources.
Tool
Description
Power Pivot
A robust business analytics tool that provides advanced data modeling and analysis capabilities.
Unlocking New Insights with Visualizations
Microsoft Excel offers an array of visualization tools, including charts, graphs, and tables. These visualizations allow users to present complex data in a clear and concise manner, facilitating better decision-making and communication.
Empowering Decision-Making through Data Analysis
By leveraging Microsoft Excel’s advanced features and powerful data analysis capabilities, individuals can gain valuable insights into their data. This enables informed decision-making, improved productivity, and enhanced overall performance.
The Power of Collaboration in Excel
Microsoft Excel offers a range of collaboration tools, including real-time commenting, @mentions, and co-authoring. These features enable multiple users to work together seamlessly on spreadsheets, streamlining teamwork and improving project outcomes.
Unlocking the Full Potential of Microsoft Excel: Take Control of Your Data Today
By harnessing the power of Microsoft Excel, individuals can unlock new insights into their data, making informed decisions and driving business success. Whether used for personal organization, educational purposes, or professional development, Microsoft Excel remains an indispensable tool in today’s fast-paced world.
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Processor: next-gen chip for heavy physics processing
RAM: 32 GB to avoid micro-stutters
Disk Space: at least 100 GB for open-world titles
Graphic Processor: RTX 3060 or RX 6600 for minimum settings
Surviving on Xylophia-IV: A Desperate Struggle for Existence
In the unforgiving vastness of space, there exists a planet so volatile that even the most hardened astronauts wouldn’t dare to tread upon its surface. Xylophia-IV, a world shrouded in perpetual chaos, is home to a multitude of catastrophic threats waiting to be unleashed upon an unsuspecting populace. It’s here, on this forsaken rock, that you’ll find yourself fighting for survival amidst an onslaught of extraterrestrial forces.• Manage your resources wisely: Build automated fortresses, extract vital minerals and resources, and allocate them effectively to fuel your defense.• Harness the power of elemental abilities: Unleash devastating attacks with a vast arsenal of high-tech weaponry, including energy blasts and particle cannons.• Construct defensive turrets: Customize your base’s defenses with an array of destructive turrets, each capable of unleashing its unique brand of havoc upon enemy forces.| Enemy Type | Description | Recommended Turret Placement || — | — | — || Xylophian Drone Swarms | Swarms of deadly drones that can overwhelm a single point of defense | Position turrets near entrances to funnel swarms into killing zones. || Gravity Worms | Burrowing creatures capable of warping space-time | Place turrets above or beneath wormholes to intercept them. |
Forming Alliances and Taming the Wilderness
As you delve deeper into Xylophia-IV’s unforgiving landscape, you’ll discover that forming alliances with fellow survivors is key to survival. Join forces with friends to develop a comprehensive base defense strategy, leveraging each other’s strengths and weaknesses.• Establish trade routes: Exchange valuable resources with allied bases to bolster your arsenal.• Explore the wilderness: Tame Xylophia-IV’s beautiful yet deadly environment by charting uncharted territories and discovering hidden secrets.
A New Home Amidst the Ruins
On this unforgiving world, you’ll find a sense of belonging among those who share your determination to survive. As you build a new home amidst the ruins of Xylophia-IV’s cataclysmic past, remember that even in the darkest corners of space, there exists hope for redemption and rebirth.• Cultivate friendships: Nurture bonds with fellow survivors to create an unbreakable network of support.• Contribute to the community: Share your knowledge, expertise, and resources to build a brighter future on Xylophia-IV.
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Processor: 1 GHz, 2-core minimum
RAM: 4 GB recommended
Disk space: At least 64 GB
Unlocking the Power of MATLAB
MATLAB is a leading programming platform that has revolutionized the way we approach numerical computing and analysis. With its extensive library of tools and techniques, it enables users to efficiently solve complex problems in various fields. The platform offers unparalleled flexibility and scalability, making it an ideal choice for researchers, engineers, and students alike. From data visualization to machine learning, MATLAB provides a comprehensive framework for developing innovative solutions. Its robust toolboxes and libraries ensure seamless integration with other software systems. By harnessing the power of MATLAB, users can accelerate their workflow, improve productivity, and uncover new insights.
Key Features and Applications
• Matrix operations: Efficiently perform matrix algebraic calculations, including linear algebra, eigendecomposition, and singular value decomposition.• Graphing: Visualize complex data structures using various plot types, including 2D and 3D plots, heatmap, and scatter plots.• Mathematical modeling: Develop and solve mathematical models to analyze real-world phenomena, such as population dynamics, optimization problems, and signal processing.
MATLAB Toolbox
Description
Machine Learning Toolbox
Predictive modeling and machine learning algorithms for classification, regression, clustering, and more.
Signal Processing Toolbox
Real-time signal processing, filtering, and analysis using advanced techniques like spectral estimation.
FAQs and Troubleshooting Tips
Q: What is the difference between MATLAB and other programming languages?A: MATLAB offers a unique combination of high-level syntax, built-in numerical libraries, and graphical user interface (GUI), making it an ideal choice for numerical computing and data analysis.Q: Can I use MATLAB on Windows or macOS?A: Yes, MATLAB can be installed on both Windows and macOS operating systems. However, some features may require specific system configurations.
Real-World Applications of MATLAB
• Scientific Research: MATLAB is widely used in various scientific disciplines, including physics, engineering, biology, and chemistry.• Data Analysis: Businesses and organizations use MATLAB to analyze large datasets, visualize trends, and gain insights into customer behavior.• Simulation and Modeling: MATLAB is employed in industries like aerospace, automotive, and energy to simulate complex systems and predict behavior.
Conclusion and Next Steps
MATLAB has become an essential tool for anyone looking to accelerate their workflow, improve productivity, and unlock new insights. With its extensive library of tools and techniques, it provides unparalleled flexibility and scalability. Whether you’re a researcher, engineer, or student, MATLAB offers a comprehensive framework for developing innovative solutions.
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Processor: 4.0 GHz+ boost clock recommended for CPU inference
RAM: at least 32 GB in dual-channel mode for bandwidth
Disk: 150+ GB for high-context vector database storage
Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading
The Pioneering Voice of Qwen3-TTS-12Hz-1.7B-CustomVoice
Qwen3-TTS-12Hz-1.7B-CustomVoice is a groundbreaking text-to-speech model that has revolutionized the way we experience voice synthesis. Its cutting-edge technology delivers high-fidelity voice output at an unprecedented 12 Hz frame rate, providing users with unparalleled realism and nuance. By harnessing the power of custom voice cloning, this model enables users to create personalized speech that not only retains the speaker’s unique characteristics but also infuses them with a sense of authenticity.The model’s 1.7 B parameter architecture strikes a delicate balance between performance and memory footprint, making it an ideal choice for deployment on consumer-grade hardware. Moreover, its inference latency of under 50 ms per utterance ensures seamless real-time applications such as interactive assistants and live dubbing. With its extensive support for multiple languages and prosodic styles, Qwen3-TTS-12Hz-1.7B-CustomVoice has set a new standard in voice synthesis, enabling users to create a wide range of engaging narratives.
Technical Specifications
Specification
Value
1.7 B
Sample Rate
12 Hz (frame)
Training Data
200 h multi-speaker speech
Latency
50 ms
Supported Languages
20+
Frequently Asked Questions
Q: What makes Qwen3-TTS-12Hz-1.7B-CustomVoice a unique text-to-speech model?A: Its custom voice cloning feature allows users to create personalized speech that retains the speaker’s unique characteristics.Q: How does the model’s 1.7 B parameter architecture impact its performance and memory footprint?A: The model strikes a delicate balance between performance and memory footprint, making it suitable for deployment on consumer-grade hardware.Q: What is the inference latency of Qwen3-TTS-12Hz-1.7B-CustomVoice per utterance?A: Inference latency stays under 50 ms per utterance, enabling real-time applications such as interactive assistants and live dubbing.Q: Can I use Qwen3-TTS-12Hz-1.7B-CustomVoice for commercial purposes?A: Yes, the model has been optimized for multiple languages and prosodic styles, producing natural-sounding output across a wide range of domains.
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CPU: multi-threading optimized for fast prompt processing
RAM: 64 GB to avoid OOM crashes on large contexts
Storage: extra room for future model updates and datasets
Graphics: stable 30+ tk/s at 4-bit quantization on medium setup
The Revolutionary Tiny- Random-GPT2 Language Model
The tiny-random-gpt2 is a game-changing, compact language model designed to accelerate inference on consumer hardware. This innovative approach yields significant reductions in parameter count compared to standard GPT‑2 variants. The model’s randomized initialization strategy prioritizes speed over accuracy, making it an attractive solution for real-time applications. With its cutting-edge architecture, the tiny-random-gpt2 is poised to revolutionize the field of natural language processing.
Technical Specifications and Performance Benchmarks
Context Window Length:
256 tokens
Training Data Size:
About 1TB of text data
Token Generation Speed:
Over 100 tokens per second on a single CPU core
Model Specifications:
Description
Parameters:
2M, compact and efficient architecture.
Training Data Size:
About 1TB of text data, diverse internet-scale corpus.
Token Generation Speed:
Over 100 tokens per second on a single CPU core, rapid inference capabilities.
Frequently Asked Questions
What makes the tiny-random-gpt2 language model unique?
The combination of compact architecture and fast inference capabilities make it an attractive solution for real-time applications.
How does the randomized initialization strategy impact performance?
Prioritizing speed over accuracy allows for faster processing times, making it suitable for dynamic environments.
Conclusion and Future Directions
The tiny-random-gpt2 is an innovative language model that offers significant advantages in terms of compactness, performance, and inference speed. As natural language processing continues to evolve, the potential applications of this technology are vast, from real-time language translation to conversational AI systems. With ongoing research and development, we can expect to see further improvements in accuracy and efficiency, solidifying the tiny-random-gpt2 as a leading player in the field.
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RAM: high-speed DDR5 memory preferred for CPU offloading
Storage:100 GB free space for HuggingFace cache folder
Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration
Unlocking Efficient AI with Qwen3.6-35B-A3B-MLX-4bit
The Qwen3.6-35B-A3B-MLX-4bit model represents a significant advancement in open-source language models, delivering strong performance while maintaining a compact footprint. Built on the A3B architecture, it leverages 4-bit MLX quantization to achieve efficient inference on consumer-grade hardware. With 35 billion parameters and an 8K token context window, the model excels at both reasoning and generation tasks. It supports multi-language understanding and integrates seamlessly with the MLX ecosystem for optimized deployment.
The combination of high capacity and low-bit quantization makes Qwen3.6-35B-A3B-MLX-4bit an attractive choice for developers seeking powerful yet resource-friendly AI solutions.
Key Considerations
1. **Reasoning Capabilities**: With its 8K token context window, the model excels at complex reasoning tasks.2. **Generation Quality**: The Qwen3.6-35B-A3B-MLX-4bit model delivers high-quality generation outputs, making it suitable for various applications.
Q&A
What is the primary advantage of using Qwen3.6-35B-A3B-MLX-4bit in AI development?
The 4-bit MLX quantization allows for efficient inference on consumer-grade hardware.
How does the model’s context length impact its performance?
The 8K token context window enables the model to handle complex reasoning tasks effectively.
Next Steps
1. **Model Deployment**: Integrate Qwen3.6-35B-A3B-MLX-4bit into your AI development pipeline for optimized performance.2. **Customization**: Explore customizing the model to meet specific application requirements, such as multi-language support or specialized quantization schemes.3. **Further Development**: Continuously monitor and improve the model’s capabilities to ensure it remains a competitive choice in AI development.
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Trapped in Time’s Labyrinth
With sixty-six days left to live, Soul must embark on a perilous quest for vengeance against those who wronged him. The dark steampunk world of Wuxia unfolds like a living, breathing entity, where ancient traditions and cutting-edge technology converge in a swirling vortex of violence and deception. As the protagonist navigates this treacherous landscape, he must confront his own troubled past and face the very real possibility that he may not escape the underworld alive.
The Art of Kungfupunk
• Master precise parries to dodge deadly attacks• Chain strikes to unleash devastating combos• Finishers to seal your opponent’s fateOr, alternatively:1. Employ your wits to outmaneuver foes with cunning trapsets and decoy tactics2. Craft intricate weapons from scavenged parts to confound and disorient enemies3. Unleash a maelstrom of projectiles to scatter opponents and gain the upper hand
A Web of Deceit
As Soul delves deeper into the underworld, he uncovers a complex web of intrigue and betrayal that threatens to consume him whole. A mysterious figure known only as “The Archon” pulls the strings from behind the scenes, manipulating events to suit their own sinister agenda.
Unraveling the Conspiracy
• Follow cryptic clues to uncover hidden truths• Forge uneasy alliances with unlikely allies to further your goals• Infiltrate high-security facilities to gather vital intelligenceOr, instead:1. Use stealth and deception to evade detection and stay one step ahead of your pursuers2. Utilize your knowledge of the underworld’s underworld to navigate treacherous social hierarchies3. Employ advanced hacking tools to access restricted systems and uncover classified information
A Desperate Quest for Redemption
With time running out, Soul must confront his own demons and make a choice: seek revenge or find a way to escape the underworld’s deadly clutches. Will he succumb to his thirst for vengeance, or can he forge a new path that leads him towards redemption? The fate of the underworld hangs in the balance, as one man’s quest for justice becomes a fight for survival.
Embracing the Unpredictable
• Adapt to changing circumstances and exploit weaknesses in your enemies• Harness the power of your surroundings to turn the tables on your foes• Trust your instincts and let your training take over in the heat of battle
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CPU: modern architecture (Zen 3 / Alder Lake minimum)
RAM: high-speed DDR5 memory preferred for CPU offloading
Disk Space: 80 GB NVMe SSD required for fast model weights loading
Graphics: stable 30+ tk/s at 4-bit quantization on medium setup
The Rise of Efficient AI: Unlocking Qwen3.5-27B-AWQ-4bit’s Potential
The Qwen3.5-27B-AWQ-4bit model is a groundbreaking achievement in the realm of natural language processing, boasting an unprecedented 27 billion parameters that have been finely tuned for optimal performance on consumer hardware. This cutting-edge architecture leverages advanced quantization techniques to reduce memory footprint while preserving remarkable strength across various multilingual tasks. With its innovative approach to model optimization, Qwen3.5-27B-AWQ-4bit is poised to revolutionize the field of AI.
Unpacking Key Features and Benchmarks
•
Parameter Count: 27 billion parameters, designed for efficient inference on consumer hardware
Context Length: Supports a 2048-token context window, enabling coherent long-form generation and reasoning
Value
Parameter Count
27 B
Quantization
AWQ 4-bit
Context Length
2048 tokens
Typical Latency (GPU)
~120 ms per 100 tokens
Competitive Results and Future Outlook
• The Qwen3.5-27B-AWQ-4bit model has demonstrated competitive results in various benchmarks, often matching larger models within a few percentage points.• Benchmarks show remarkable performance on MMLU, GSM-8K, and Commonsense Reasoning tasks, solidifying its position as a top-tier AI model.
What Does This Mean for Production Deployments?
The Qwen3.5-27B-AWQ-4bit model offers an enticing trade-off between size, speed, and accuracy, making it an attractive choice for production deployments. By striking this balance, developers can unlock new possibilities in areas such as language translation, text summarization, and conversational AI.
Conclusion: Unlocking Qwen3.5-27B-AWQ-4bit’s Full Potential
In conclusion, the Qwen3.5-27B-AWQ-4bit model represents a significant breakthrough in the pursuit of efficient AI. By leveraging advanced techniques such as AWQ and context window optimization, this model is poised to transform various industries and applications, providing unparalleled value for developers and end-users alike.
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)
