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#Finetuning Reel by @dailydoseofds_ - 5 techniques to fine-tune LLMs, explained visually 🧠

Traditional fine-tuning (adjusting all parameters) is infeasible with LLMs. These models have b
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@dailydoseofds_
5 techniques to fine-tune LLMs, explained visually 🧠 Traditional fine-tuning (adjusting all parameters) is infeasible with LLMs. These models have billions of parameters and are hundreds of GBs in size, and not everyone has access to such computing infrastructure. Thankfully, today, we have many optimal ways to fine-tune LLMs. Five popular techniques are depicted below: 1️⃣ LoRA Add two low-rank matrices, A and B, alongside weight matrices W, which contain the trainable parameters. Instead of fine-tuning W, adjust the updates in these low-rank matrices. 2️⃣ LoRA-FA While LoRA considerably decreases total trainable parameters, it still demands high activation memory to update low-rank weights. LoRA-FA (FA = Frozen-A) freezes matrix A and only updates matrix B. 3️⃣ VeRA In LoRA, every layer has different A and B matrices, both trained. In VeRA, matrices A and B are frozen, random, and shared across all layers. VeRA learns small, layer-specific scaling vectors (b and d) as the only trainable parameters. 4️⃣ Delta-LoRA In addition to training low-rank matrices, matrix W is also adjusted but not traditionally. The difference (delta) between the product of A and B in two consecutive training steps is added to W. 5️⃣ LoRA+ In LoRA, both matrices A and B are updated with the same learning rate. Authors found that setting a higher learning rate for matrix B results in more optimal convergence. πŸ‘‰ Over to you: Which fine-tuning technique have you tried? #ai #llm #finetuning
#Finetuning Reel by @girlwhodebugs (verified account) - RAG vs Fine Tuning and When to choose which?
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#rag #finetuning #ai #ml #tech
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@girlwhodebugs
RAG vs Fine Tuning and When to choose which? . . . . . #rag #finetuning #ai #ml #tech
#Finetuning Reel by @qubitship - Fine-tuning AI in 60 seconds πŸ€–βš‘

Fine-tuning is one of the most powerful techniques in AI and machine learning. Instead of training a model from scra
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@qubitship
Fine-tuning AI in 60 seconds πŸ€–βš‘ Fine-tuning is one of the most powerful techniques in AI and machine learning. Instead of training a model from scratch, you take a pre-trained model and train it further on specific data so it performs better on a particular task. In this quick reel, you’ll learn the basic idea of fine-tuning in under a minute. Perfect for developers, AI enthusiasts, and anyone curious about how modern AI models get smarter. Follow for more AI, coding, and tech content πŸš€ #ai #machinelearning #finetuning #artificialintelligence #education
#Finetuning Reel by @mar_antaya (verified account) - yay for more finetuning content πŸ˜‡ #finetuning #llms #developer
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yay for more finetuning content πŸ˜‡ #finetuning #llms #developer
#Finetuning Reel by @whats_ai (verified account) - If your model is hallucinating about your company docs, fine-tuning is usually not the fix.

That's the trap.

A lot of teams see wrong answers about
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@whats_ai
If your model is hallucinating about your company docs, fine-tuning is usually not the fix. That’s the trap. A lot of teams see wrong answers about internal files and assume they need to retrain the model. But fine-tuning changes behavior, not factual recall of constantly changing company knowledge. It can help with tone, structure, or broad domain patterns. It is not the best tool for making a model reliably remember your latest return policy, pricing sheet, or product catalog. For that, you usually want retrieval. In other words: fine-tuning teaches patterns, retrieval supplies facts. So if the issue is accuracy on specific documents, give the model better access to the right context instead of trying to bake those facts into its parameters. It is cheaper, easier to update, and much more controllable. Mixing those two up is one of the fastest ways to waste time and budget in AI. Have you seen teams make this mistake already? I’m Louis-FranΓ§ois, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup πŸš€ #AI #LLM #FineTuning #RAG #Hallucinations #AIEducation #GenerativeAI #MachineLearning #AIForBusiness #Embeddings #AITips #RetrievalAugmentedGeneration
#Finetuning Reel by @sarvam_everything - Harvard Study Reveals God's Signature In The Universe

A groundbreaking study from Harvard says science may have found proof of God. 🌌✝️

Physicists
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Harvard Study Reveals God's Signature In The Universe A groundbreaking study from Harvard says science may have found proof of God. 🌌✝️ Physicists have discovered the universe is impossibly "fine-tuned." If gravity or quantum forces were off by a razor's edge, nothing would existβ€”no stars, no planets, no us. The odds of this happening by random chance are zero. This video breaks down the stunning evidence that our universe isn't an accident. The math is pointing to a designer. Watch before it's taken down. #God #Science #Universe #Physics #Harvard #FineTuning #Debate proof of god, fine-tuning universe, harvard god study, science and religion, intelligent design, why does the universe exist, physics proves god, antimatter god, universe is a design
#Finetuning Reel by @wdf_ai - No GPU? No problem.
Fine-tune or experiment with AI Models directly on Google's cloud GPU - inside VS Code.

Comment "GPU" and I'll DM you the link.
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No GPU? No problem. Fine-tune or experiment with AI Models directly on Google’s cloud GPU - inside VS Code. Comment β€œGPU” and I’ll DM you the link. #aiengineering #machinelearning #llm #finetuning #python​​​​​​​​​​​​​​​​
#Finetuning Reel by @koshurai.official - 99% of people using AI tools don't understand why their outputs feel generic.

It's not the prompt. It's the model.

Fine-tuning an LLM is the differe
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@koshurai.official
99% of people using AI tools don't understand why their outputs feel generic. It's not the prompt. It's the model. Fine-tuning an LLM is the difference between a Swiss Army knife and a scalpel. Here's what most people get wrong about it: β†’ Pre-trained β‰  production-ready LLMs trained on the internet know everything β€” and nothing specific to YOUR domain. β†’ Fine-tuning = precision, not retraining You don't rebuild the model. You redirect it using a small, task-specific dataset. β†’ It shapes tone, not just knowledge Fine-tuning teaches the model how to speak β€” not just what to say. Medical. Legal. Casual. Technical. β†’ Less data than you think A few hundred high-quality examples can outperform a generic GPT prompt every single time. β†’ It's the moat most companies are ignoring Anyone can write a prompt. Not everyone can deploy a model trained on their own data. The takeaway: Prompting is renting. Fine-tuning is owning. If you're building AI products in 2026 and skipping fine-tuning β€” you're leaving your biggest competitive edge on the table. πŸ’¬ Are you fine-tuning models in your work, or still relying purely on prompting? #LLMs #FineTuning #GenerativeAI #DataScience #MachineLearning
#Finetuning Reel by @ecehanersoz (verified account) - #deepseek #ai #llm #learning #reinforcementlearning #finetuning #optimization
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#deepseek #ai #llm #learning #reinforcementlearning #finetuning #optimization
#Finetuning Reel by @pvergadia (verified account) - Most AI teams are choosing wrong between RAG and fine-tuning. 

Here's the 60-second answer πŸ‘‡

RAG = keep the model frozen, feed it your data at quer
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@pvergadia
Most AI teams are choosing wrong between RAG and fine-tuning. Here's the 60-second answer πŸ‘‡ RAG = keep the model frozen, feed it your data at query time. You change the context, not the weights. Fine-tuning = update the model's weights on your own examples so it learns your style, rules, and domain. Use RAG when: β†’ Your data changes often β†’ It lives in docs or databases β†’ You need factual, up-to-date answers fast and cheap Use fine-tuning when: β†’ You need consistent tone or strict formats β†’ Latency and cost per request actually matter β†’ RAG alone isn't fixing the behavior The biggest mistake is jumping straight to fine-tuning before even trying a solid RAG setup. Save this before your next AI build. πŸ‘‡ [rag, finetuning, llm, ai, genai, machinelearning, mlops, llmops, retrieval, embeddings, vectordatabase, aiengineering, python, langchain, openai, azure, cloudai, prompt, aistrategy, aiproduct, fyp, viral, trending, explorepage] #rag #finetuning #llm #genai #aiengineering
#Finetuning Reel by @madhav_somani - RAG vs. Fine-Tuning: Which one do you actually need? πŸ€–
Stop over-complicating your AI strategy. Whether you're building a side project or an enterpri
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RAG vs. Fine-Tuning: Which one do you actually need? πŸ€– Stop over-complicating your AI strategy. Whether you’re building a side project or an enterprise-grade solution at a place like Microsoft, picking the right approach is 80% of the battle. Think of it this way: βœ… RAG is like giving your AI an open-book exam. It’s great for facts, live data, and reducing those β€œhallucination” moments. βœ… Fine-Tuning is like intensive training to change how the AI speaks, its style, and its deep-rooted behavior. The Golden Rule: πŸ“ Need updated knowledge? Go RAG. πŸ“ Need a specific tone or format? Go Fine-Tuning. Drop a β€œRAG” or β€œFT” in the comments to let me know which one you’re currently experimenting with! πŸ‘‡ #SoftwareEngineer #GenerativeAI #RAG #FineTuning #LLM TechCareer
#Finetuning Reel by @kodekloud (verified account) - Prompt engineering is a lie. Here's what actually works πŸ‘‡

Billions of dollars went into building GPT-4, Claude, and Gemini - but they're built to be
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@kodekloud
Prompt engineering is a lie. Here's what actually works πŸ‘‡ Billions of dollars went into building GPT-4, Claude, and Gemini β€” but they're built to be generalists. The moment you need an AI agent that ALWAYS responds in JSON, stays in character as a medieval NPC, or follows strict brand guidelines… prompts start failing. Why? Because prompts can be hacked. Users inject instructions, override your system prompt, and break the experience. Fine-tuning is different. It rewires the model's parameters β€” embedding behavior directly into how it thinks. Pair that with techniques like LoRA & QLoRA, and you can fine-tune models on consumer hardware. No data center required. 🧠 Quick rule: Fine-tuning teaches HOW to behave. RAG pulls WHAT to know. Save this for your next AI project πŸ”– Follow @kodekloud for daily AI, DevOps & Cloud breakdowns. . . . #FineTuning #PromptEngineering #AIAgents #LLM #LoRA #QLoRA #MachineLearning #GenerativeAI #RLHF #ChatGPT #ClaudeAI #GeminiAI #AIEngineering #DevOps #MLOps #KodeKloud #AIShorts #TechReels #AITutorial #ArtificialIntelligence

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