
Teacher
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Teacher
@RealLeaderPK
Relevant degrees.certifications or specific exams you help with.


🚀 Meet LLaDA2.2-flash — the first agent-oriented MoE Diffusion LLM. Levenshtein editing—deletion, insertion, and substitution—enables self-correction during decoding. A more elegant solution to model collapse than forcing diffusion back into left-to-order generation. Performance: ⚡ 703.82 TPS on BFCL-V4 function calling 519.0 TPS on SWE-bench Verified coding ⚡ 1.64x BF16 throughput vs autoregressive models Built for high-throughput, low-latency AI agents. 🔗 GitHub: github.com/inclusionAI/LL… 🤗 Hugging Face: huggingface.co/inclusionAI/LL… 📄 Technical Report:github.com/inclusionAI/LL… #LLaDA #OpenSource #DiffusionLLM #AIAgents

🚀 Meet LLaDA2.2-flash — the first agent-oriented MoE Diffusion LLM. Levenshtein editing—deletion, insertion, and substitution—enables self-correction during decoding. A more elegant solution to model collapse than forcing diffusion back into left-to-order generation. Performance: ⚡ 703.82 TPS on BFCL-V4 function calling 519.0 TPS on SWE-bench Verified coding ⚡ 1.64x BF16 throughput vs autoregressive models Built for high-throughput, low-latency AI agents. 🔗 GitHub: github.com/inclusionAI/LL… 🤗 Hugging Face: huggingface.co/inclusionAI/LL… 📄 Technical Report:github.com/inclusionAI/LL… #LLaDA #OpenSource #DiffusionLLM #AIAgents

🚀 Meet LLaDA2.2-flash — the first agent-oriented MoE Diffusion LLM. Levenshtein editing—deletion, insertion, and substitution—enables self-correction during decoding. A more elegant solution to model collapse than forcing diffusion back into left-to-order generation. Performance: ⚡ 703.82 TPS on BFCL-V4 function calling 519.0 TPS on SWE-bench Verified coding ⚡ 1.64x BF16 throughput vs autoregressive models Built for high-throughput, low-latency AI agents. 🔗 GitHub: github.com/inclusionAI/LL… 🤗 Hugging Face: huggingface.co/inclusionAI/LL… 📄 Technical Report:github.com/inclusionAI/LL… #LLaDA #OpenSource #DiffusionLLM #AIAgents

🚀 Meet LLaDA2.2-flash — the first agent-oriented MoE Diffusion LLM. Levenshtein editing—deletion, insertion, and substitution—enables self-correction during decoding. A more elegant solution to model collapse than forcing diffusion back into left-to-order generation. Performance: ⚡ 703.82 TPS on BFCL-V4 function calling 519.0 TPS on SWE-bench Verified coding ⚡ 1.64x BF16 throughput vs autoregressive models Built for high-throughput, low-latency AI agents. 🔗 GitHub: github.com/inclusionAI/LL… 🤗 Hugging Face: huggingface.co/inclusionAI/LL… 📄 Technical Report:github.com/inclusionAI/LL… #LLaDA #OpenSource #DiffusionLLM #AIAgents



















