deepseek-ai/DeepSeek-R1-0528
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Qwen 3 models released🔥
It offers 2 MoE and 6 dense models with following parameter sizes: 0.6B, 1.7B, 4B, 8B, 14B, 30B(MoE), 32B, and 235B(MoE).
Models: Qwen/qwen3-67dd247413f0e2e4f653967f
Blog: https://qwenlm.github.io/blog/qwen3/
Demo: Qwen/Qwen3-Demo
GitHub: https://github.com/QwenLM/Qwen3
✅ Pre-trained 119 languages(36 trillion tokens) and dialects with strong translation and instruction following abilities. (Qwen2.5 was pre-trained on 18 trillion tokens.)
✅Qwen3 dense models match the performance of larger Qwen2.5 models. For example, Qwen3-1.7B/4B/8B/14B/32B perform like Qwen2.5-3B/7B/14B/32B/72B.
✅ Three stage done while pretraining:
• Stage 1: General language learning and knowledge building.
• Stage 2: Reasoning boost with STEM, coding, and logic skills.
• Stage 3: Long context training
✅ It supports MCP in the model
✅ Strong agent skills
✅ Supports seamless between thinking mode (for hard tasks like math and coding) and non-thinking mode (for fast chatting) inside chat template.
✅ Better human alignment for creative writing, roleplay, multi-turn conversations, and following detailed instructions.
It offers 2 MoE and 6 dense models with following parameter sizes: 0.6B, 1.7B, 4B, 8B, 14B, 30B(MoE), 32B, and 235B(MoE).
Models: Qwen/qwen3-67dd247413f0e2e4f653967f
Blog: https://qwenlm.github.io/blog/qwen3/
Demo: Qwen/Qwen3-Demo
GitHub: https://github.com/QwenLM/Qwen3
✅ Pre-trained 119 languages(36 trillion tokens) and dialects with strong translation and instruction following abilities. (Qwen2.5 was pre-trained on 18 trillion tokens.)
✅Qwen3 dense models match the performance of larger Qwen2.5 models. For example, Qwen3-1.7B/4B/8B/14B/32B perform like Qwen2.5-3B/7B/14B/32B/72B.
✅ Three stage done while pretraining:
• Stage 1: General language learning and knowledge building.
• Stage 2: Reasoning boost with STEM, coding, and logic skills.
• Stage 3: Long context training
✅ It supports MCP in the model
✅ Strong agent skills
✅ Supports seamless between thinking mode (for hard tasks like math and coding) and non-thinking mode (for fast chatting) inside chat template.
✅ Better human alignment for creative writing, roleplay, multi-turn conversations, and following detailed instructions.
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Agentic AI Era: Analyzing MCP vs MCO 🚀
Hello everyone!
With the rapid advancement of AI agent technology, two architectures have come into the spotlight: MCP (Model Context Protocol) and MCO (Model Context Open-json). Today, we’ll introduce the key features and differences of these two approaches.
VIDraft/Agentic-AI-CHAT
MCP: The Traditional Approach 🏛️
Centralized Function Registry: All functions are hardcoded into the core system.
Static Function Definitions & Tight Coupling: New features require changes to the core application code, limiting scalability.
Monolithic Design: Complex deployment and version management can cause a single error to affect the whole system.
Code Example:
'''py
FUNCTION_REGISTRY = {
"existing_function": existing_function,
"new_function": new_function # Adding a new function
}
'''
MCO: A Revolutionary Approach 🆕
JSON-based Function Definitions: Function details are stored in external JSON files, enabling dynamic module loading.
Loose Coupling & Microservices: Each function can be developed, tested, and deployed as an independent module.
Flexible Scalability: Add new features by simply updating the JSON and module files, without modifying the core system.
JSON Example:
[
{
"name": "analyze_sentiment",
"module_path": "nlp_tools",
"func_name_in_module": "sentiment_analysis",
"example_usage": "analyze_sentiment(text=\"I love this product!\")"
}
]
Why MCO? 💡
Enhanced Development Efficiency: Developers can focus on their own modules with independent testing and deployment.
Simplified Error Management: Errors remain confined within their modules, enabling quick hotfixes.
Future-Proofing: With potential features like remote function calls (RPC), access control, auto-documentation, and a function marketplace, MCO paves the way for rapid innovation.
Practical Use & Community 🤝
The MCO implementation has been successfully tested on Vidraft’s LLM (based on Google Gemma-3)
Hello everyone!
With the rapid advancement of AI agent technology, two architectures have come into the spotlight: MCP (Model Context Protocol) and MCO (Model Context Open-json). Today, we’ll introduce the key features and differences of these two approaches.
VIDraft/Agentic-AI-CHAT
MCP: The Traditional Approach 🏛️
Centralized Function Registry: All functions are hardcoded into the core system.
Static Function Definitions & Tight Coupling: New features require changes to the core application code, limiting scalability.
Monolithic Design: Complex deployment and version management can cause a single error to affect the whole system.
Code Example:
'''py
FUNCTION_REGISTRY = {
"existing_function": existing_function,
"new_function": new_function # Adding a new function
}
'''
MCO: A Revolutionary Approach 🆕
JSON-based Function Definitions: Function details are stored in external JSON files, enabling dynamic module loading.
Loose Coupling & Microservices: Each function can be developed, tested, and deployed as an independent module.
Flexible Scalability: Add new features by simply updating the JSON and module files, without modifying the core system.
JSON Example:
[
{
"name": "analyze_sentiment",
"module_path": "nlp_tools",
"func_name_in_module": "sentiment_analysis",
"example_usage": "analyze_sentiment(text=\"I love this product!\")"
}
]
Why MCO? 💡
Enhanced Development Efficiency: Developers can focus on their own modules with independent testing and deployment.
Simplified Error Management: Errors remain confined within their modules, enabling quick hotfixes.
Future-Proofing: With potential features like remote function calls (RPC), access control, auto-documentation, and a function marketplace, MCO paves the way for rapid innovation.
Practical Use & Community 🤝
The MCO implementation has been successfully tested on Vidraft’s LLM (based on Google Gemma-3)
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