Zoumana KEITA

Wrote 146 lines of Python that spin up a local LLM API server in about 5 minutes to call it OpenAI style. Tested it with #gpt-oss-120b on an 8-GPU box, and with #gpt-oss-20b and #LLaMA on a single GPU or even CPU—runs fine. I originally put this together for a project last year, then ended up reusing it so often I figured I’d just drop it here. It’s not tuned for memory, but it’ll sniff out whatever GPUs you’ve got and run them in parallel. Simple, clean, and it works. Install it with pip: pip install min-llm-server-client Tutorial: https://lnkd.in/eauR6h9s Source code: https://lnkd.in/gA_qJjdi

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🚨 If you're a Data Scientist or ML Engineer still using just ONE AI model... you're limiting your potential. Here's why. 🌟 The latest AI benchmarks (AIME 2025, SWE-Bench, MMLU) revealed something that will change how you approach AI tools in 2025: The "One Model for Everything" myth is DEAD. Here's what the data actually shows: 🎯 For Coding & Debugging: → Claude 4 dominates with 70% on SWE-Bench → Its "Extended Thinking" mode is like having a senior dev review your code 🧮 For Math & Complex Reasoning: → Gemini 2.5 Pro scored 86.7% on AIME 2025 → It literally thinks through problems step-by-step (no external tools needed!) 💬 For General Knowledge & Conversation: → ChatGPT GPT-4.5 leads with 90.2% on MMLU → Feels like talking to a thoughtful colleague, not a robot 📚 For Long Documents & Research: → Gemini's 1 million token context = analyzing entire books at once → Claude's 200K tokens handles most real-world scenarios perfectly ⚡ For Real-Time Knowledge: → Grok 3 connects directly to X (Twitter) for live updates → Perfect for trend analysis and breaking news insights Here's the contrarian truth most people miss: Instead of debating "which AI is best," top performers are using multi-model strategies. They're picking Claude for coding sessions, Gemini for research deep-dives, and ChatGPT for client communications. The smartest move in 2025? Use platforms that give you access to ALL models in one place. Switch between them based on the task, not loyalty to one brand. Stop limiting yourself to one AI when you could have the best of all worlds. Your current approach probably looks like this: ❌ Stick with one model for everything ❌ Accept "good enough" results ❌ Waste time switching between different platforms Try this instead: ✅ Match the model to the specific task ✅ Use Claude for technical work, Gemini for analysis, GPT for communication ✅ Access everything from one unified interface The AI landscape evolved faster than most people realize. Your toolkit should too. What's your current AI workflow? Are you still married to one model, or have you started experimenting with a multi-model approach? Share your thoughts below – I'm curious to hear which combinations are working best for your specific use cases! 👇

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🚨 𝐍-𝐠𝐫𝐚𝐦𝐬 𝐀𝐫𝐞 𝐁𝐚𝐜𝐤 - 𝐁𝐮𝐭 𝐚𝐬 𝐌𝐞𝐦𝐨𝐫𝐲, 𝐍𝐨𝐭 𝐌𝐨𝐝𝐞𝐥𝐬 For years, Transformers have simulated retrieval through computation. Every time a model recognizes “Alexander the Great” or a formulaic phrase, it burns layers of attention + FFNs to reconstruct something that is fundamentally static. A new Peking University × DeepSeek paper shows this is the wrong abstraction. 📌 𝐓𝐡𝐞 𝐜𝐨𝐫𝐞 𝐢𝐝𝐞𝐚 Language has two very different components: → Dynamic reasoning (needs computation) → Static local patterns (need lookup) Transformers only had the first. This paper introduces conditional memory as a new axis of sparsity - complementing MoE’s conditional computation. 📌 𝐄𝐧𝐠𝐫𝐚𝐦: 𝐍-𝐠𝐫𝐚𝐦𝐬, 𝐦𝐨𝐝𝐞𝐫𝐧𝐢𝐳𝐞𝐝 Engram revives N-grams - but not as a language model. Instead: → Hashed N-gram lookup (O(1)) → Massive embedding tables (billions of params) → Context-aware gating decides when memory matters → Memory is fused into the network, not prepended to tokens N-grams become explicit memory, not statistics. 📌 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐛𝐞𝐚𝐭𝐬 𝐩𝐮𝐫𝐞 𝐌𝐨𝐄 The paper formulates the Sparsity Allocation problem: Given fixed FLOPs and parameters, how much capacity should go to: → Experts (compute) → Memory (lookup) Result: a U-shaped scaling law. Pure MoE is suboptimal. Reallocating ~20–25% of sparse capacity to memory wins consistently. 📌 𝐓𝐡𝐞 𝐬𝐮𝐫𝐩𝐫𝐢𝐬𝐢𝐧𝐠 𝐫𝐞𝐬𝐮𝐥𝐭 Engram doesn’t just help factual recall. It improves: → General reasoning (BBH +5.0, ARC-Challenge +3.7) → Code & math (HumanEval +3.0, MATH +2.4) → Long-context retrieval (Multi-Query NIAH: 84.2 → 97.0) Why? Because early layers stop wasting depth on static reconstruction. 📌 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐭𝐢𝐜 𝐢𝐧𝐬𝐢𝐠𝐡𝐭 LogitLens + CKA show: → Engram reaches “prediction-ready” states earlier → Shallow Engram layers behave like deeper MoE layers It effectively deepens the network without adding compute. 📌 𝐒𝐲𝐬𝐭𝐞𝐦-𝐥𝐞𝐯𝐞𝐥 𝐰𝐢𝐧 Memory lookup is deterministic. That enables: → Prefetching from host memory → Overlapping communication with compute → Offloading 100B parameters with <3% throughput loss This is algorithm–system co-design done right. 📌 𝐓𝐡𝐞 𝐬𝐡𝐢𝐟𝐭 We treated all parameters as computation. This paper shows: Some parameters should just be remembered. N-grams didn’t disappear. They were waiting for the right abstraction. 📌 𝐏𝐚𝐩𝐞𝐫: https://lnkd.in/gVFgZWVU #LLMArchitecture #SparseModels #MoE #MemoryAugmentedAI #DeepLearning #AIResearch #GenAI

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DeepSeek AI Signals a New Direction in Efficient Model Design : Latest Paper Suggests a Rethink of Large-Scale Training A newly published paper on arXiv proposes structural changes to neural networks that could meaningfully improve training stability and cost efficiency and it feels like a familiar pattern. The paper introduces mHC, a technique designed to stabilize large-scale training with minimal additional compute overhead. Tested on 3B, 9B, and 27B parameter models, mHC delivered stronger benchmark performance, particularly on reasoning-heavy tasks. Notably, CEO Liang Wenfeng co-authored and personally uploaded the paper, reinforcing how hands-on DeepSeek leadership remains with core research. If this sounds familiar, it should. Similar research breadcrumbs appeared ahead of R1 and V3, both of which preceded major releases. Why this matters Last year’s “DeepSeek moment” showed how R1 approached frontier-level performance at a fraction of the cost. This paper suggests they’re still unlocking efficiency gains. Combined with greater access to advanced AI chips, these architectural improvements point to something bigger. Looking ahead to 2026 Between cost-efficient training methods and accelerating hardware access, Chinese AI releases are set to become even more competitive—not just cheaper, but smarter and more stable at scale. This doesn’t look like a one-off paper. It looks like a preview. Read the paper here : https://lnkd.in/gR222q9P #AIResearch #DeepSeek #NeuralNetworks #LLMs #AIInfrastructure #MachineLearning #AITrends

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Anthropic just exposed something massive. DeepSeek AI, Moonshot AI, and MiniMax ran industrial-scale distillation attacks against Claude: → 24,000+ fraudulent accounts → 16 million+ exchanges → Targeted Claude's most differentiated capabilities: agentic reasoning, tool use, and coding Here's what makes this technically fascinating (and terrifying): These weren't random queries. The prompts were structured for systematic capability extraction. When Anthropic released a new model during MiniMax's active campaign, MiniMax pivoted within 24 hours — redirecting nearly half their traffic to the new system. They used "hydra cluster" architectures: sprawling networks of fraudulent accounts that distribute traffic across APIs and cloud platforms. When one account gets banned, another takes its place. One proxy network managed 20,000+ accounts simultaneously, mixing distillation traffic with legitimate requests to evade detection. DeepSeek even used Claude to generate censorship-safe alternatives to politically sensitive queries — essentially training their models to dodge topics the Chinese government wants suppressed. This is model supply chain security in action. And it raises questions every AI practitioner should be thinking about: 1. If your AI agents rely on frontier model APIs, how do you validate that the model you're calling hasn't been compromised or cloned? 2. Distilled models strip safety guardrails. The capabilities transfer. The alignment doesn't. 3. The "build vs. distill" cost asymmetry is staggering. Years of R&D and hundreds of millions of dollars — replicated at a fraction of the cost. Anthropic is calling for a coordinated response across AI labs, cloud providers, and policymakers. OpenAI raised similar concerns about DeepSeek earlier this month. The window to address this is narrow. Model theft at this scale isn't just a business problem — it's an infrastructure security problem. #AI #AIAgents #ModelSecurity #DeepSeek #Anthropic #Claude #MachineLearning

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# Reproducibility In your experience, how would you make this environment decision framework more thorough Use uv if: ⚡ Blazing speed is required (10-100x faster than pip/conda) 🐍 Python-centric workflow with Julia as secondary 🪶 Minimal overhead preferred (single Rust binary) 🔄 Rapid prototyping and environment switching 📱 Modern developer tooling (lockfiles, monorepos) 🔥 Latest Python ecosystem (3.12+, newest packages) 💾 Storage constrained (much smaller than conda) Use Mamba if: 🚀 Speed is critical for daily development 🔄 Iterating frequently on environment setup 👥 Team prefers familiar tools (many scientists know conda) 📦 Mixing languages (Python + Julia + Java + R + Java + Quarto) 💻 Local development primarily 🧬 Bioinformatics/data science workflows (conda-forge ecosystem) 📟 Specific CUDA version required for PyTorch-GPU, TensorFlow-GPU, JAX-GPU. Use DevContainer / Docker if: 🔬 Long-term reproducibility is paramount (publications, dissertations) 🚀 Need cutting-edge packages (latest SciML developments) 🤝 Exact collaboration requirements ⚡ Performance critical work (no conda overhead) 🔒 Security/isolation is important 🏢 Enterprise/institutional requirements Lukas Mosser #dataScience #machineLearning #spe #SciML #docker #devcontainers #python #uv #pytorch #tensorflow

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