249.8K
PALLM Council is what you should try! 🎯
The Open AI brainjust built this new product that compares LLM Answers
#llm #openai #research
@parasmadan.in
#Llm Future Developments
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CYIntroducing LLM OS: The Future of AI-Powered Operating Systems
Designed to harness the power of advanced AI, LLM OS is set to transform how we interact with computers, offering smarter, more intuitive, and personalized experiences.
Here’s why LLM OS is the game-changer you’ve been waiting for:
🧠 AI-Driven Interface: LLM OS understands natural language, allowing you to communicate with your device through simple conversation, making complex tasks effortless.
⚡ Personalized User Experience: It learns from your preferences and habits, streamlining workflows and making your day-to-day tasks faster and more efficient.
🌍 Seamless Integration: LLM OS integrates with your favorite apps, automating processes and offering solutions based on context and intent, making multitasking easier than ever.
🔒 Enhanced Security: With AI monitoring in real-time, LLM OS anticipates and mitigates threats before they become a problem, giving you peace of mind.
#LLMOS #AI #ArtificialIntelligence #ilyasutskever #Computing #AIOS #CyberSergeAI #openAI #ssi
@cyberserge
1.5M
ARJust drop "Dataset" to access more info on this!
A massive 8 TB open dataset is now live — designed specifically for training large language models under open licenses.
What it means:
📦 One of the largest publicly available datasets for LLM training
🪪 Fully openly licensed — no copyright traps
🧠 Supports next-gen open-source models to rival GPT, Claude, Gemini
🌍 Democratizes access to LLM training infrastructure
Why it matters:
→ Training data = power in the AI race
→ Open datasets enable smaller labs, startups, and researchers to compete
→ This could fuel a new wave of open LLMs — safer, more transparent, community-driven
The future of AI may not be locked in a black box — it might be built in the open.
#OpenLLMs #AItrainingdata #ArtificialIntelligenceGlobe #OpenSourceAI #LLMdevelopment #AIinfrastructure #AIdemocratization #FutureOfAI
@artificialintelligenceglobe
55.3K
NIComment OPENSOURCE to get these AI Tools.
Running open-source AI models like DeepSeek, Mistral, and Qwen on your computer is easier than you think. Here's how you can do it:
First, let's talk about LM Studio. It's super user-friendly. You download the app from their website, then search for the model you want inside the app. Download it, and you're good to go! You can now run any AI model on your computer, fully private with unlimited usage.
Next up is Ollama. It's a bit more technical, but it's entirely open-source and free. You install the app from their website, then go back to the site to find the model you want. Copy the installation command and paste it into your terminal. This downloads the AI model onto your computer. Now, you can chat with it without needing the internet.
Here's a quick rundown of the process:
- LM Studio: Easy to use, great for beginners.
- Ollama: More technical, but offers flexibility.
Think of it like downloading a new app on your phone. You find what you want, download it, and start using it. With these tools, you can run AI models locally, keeping your data private and avoiding internet dependency.
#lmstudio #ollama #aimodels #ainews #aiindia #aicommunity #aitools #opensourceai #llm #largelanguagemodels #opensourceai
@nick_saraev
453.9K
PAA new research study came out showing how domain specific small models like this one could help future LLMs become efficient #ai #tech #education #science #research #learn #viral #fyp
@parthknowsai
13.0K
AIBypass LLM restrictions with a combination of writing in an <interaction-config> and having the LLM roleplay.
Follow @aitoolhub.co
post by tiktok/hes_a_nerd
#aicommunity #aitools #llm #ml #deepseek #chatgpt #google #nvidia #artificialintellgience
@aitoolhub.co
124.4K
PR1. Hugging Face Transformers + PEFT
The most popular ecosystem for fine-tuning LLMs. Supports LoRA, QLoRA, Adapters, Prefix Tuning, and integrates smoothly with the HF Trainer.
2. Axolotl
A production-grade framework for SFT, DPO, ORPO, RLHF, LoRA/QLoRA, and multi-GPU setups. Simple YAML configs and massive flexibility.
3. Unsloth
Ultra-fast, memory-efficient fine-tuning. Lets you train 7B–13B models on small consumer GPUs. Great for speed and low VRAM.
4. LLaMA Factory
Beginner-friendly and powerful. Supports LoRA, QLoRA, full FT, DPO, ORPO, reward modeling, plus a clean web UI for no-code fine-tuning.
5. LLaMA Efficient Tuning
Lightweight scripts tailored for LLaMA/Mistral. Supports LoRA, QLoRA, and full fine-tuning with a simple workflow.
6. Colossal-AI
A high-performance framework for large-scale, multi-GPU fine-tuning. Ideal for 30B+ models with distributed training and quantization.
#datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #llm #finetuning #generativeai
@priyal.py
1.1M
10LLM Council compares answers from multiple LLMs and ranks them anonymously.
@100xengineers
26.6K
SAWhat is a Large Language Model 🤯
How do LLMs work?
They are massive neural networks trained to predict the next word really really well.
At scale that turns into explaining ideas writing code and reasoning through problems.
#computerscience #engineering #code
@sayed.developer
45.9K
THIf I were to get started to learn LLMs — here’s exactly how I’d go about it 👇
(No degree, no prior experience needed. Just consistency and curiosity.)
Step 1: Learn Python (Weeks 1–4)
→ Do “Python for Everybody” or freeCodeCamp
→ Practice daily for 30 minutes
→ Push everything to GitHub — even your messy code
→ Start building your proof of work from Day 1
Step 2: Get ML Foundations (Weeks 5–8)
→ Take Andrew Ng’s ML course (still gold)
→ Pick 1 project on Kaggle (Titanic is great for starters)
→ Train a basic model → deploy it with Streamlit
→ Now you’ve built your first ML app 👏
Step 3: Deep Learning Phase (Months 3–4)
→ Learn through fast.ai — super hands-on
→ Fine-tune a small transformer (like BERT) on Hugging Face
→ Don’t aim for perfection, aim to finish one project well
Step 4: Understand Transformers & LLMs (Months 5–6)
→ Rebuild a mini GPT using Karpathy’s nanoGPT
→ This will help you actually understand self-attention, tokens, and training loops
→ Watch explainer videos + read blog posts to reinforce the concepts
Step 5: Learn the Real LLM Stack (Months 7–9)
→ LangChain for chaining prompts & calling tools
→ LangGraph for multi-agent workflows and memory
→ Weaviate or LanceDB for retrieval (RAG setups)
→ QLoRA for fine-tuning open models
→ vLLM for efficient inference
Step 6: Build and Publish (Months 10–12)
→ Choose one simple use case (like a research assistant or AI chatbot)
→ Build it end-to-end using the stack you’ve learned
→ Make a demo video, write a short architecture breakdown
→ Share on GitHub, LinkedIn, and Twitter — this is your new resume
Portfolio project ideas: https://www.instagram.com/p/DG6l_MrShZ8/?igsh=NTc4MTIwNjQ2YQ==
( Link also in "Learn AI" highlights)
[ai roadmap, llm learning path, genai career, how to learn ai, langchain tutorial, huggingface projects, python for ai, llm from scratch, build in public, ai agent builder, nanoGPT, deep learning 2025, vector dbs, fine tuning llms, ai portfolio project, ml roadmap, data science career guide, prompt engineering, ai learning journey]
@the.datascience.gal
30.9K
BREasily Run LLMs Locally for Free ⚡💻
Curious how to run powerful AI models right on your own computer? With LM Studio, you can browse and download open-source LLMs, chat with them in a clean interface, or even spin up a local API for your apps 🤖✨
No subscriptions, no cloud costs — just local horsepower.
I’m using it on an M3 MacBook Air and getting solid performance with mid-sized models, perfect for everyday workflows. If you want full control and zero latency worries, this is the way to go. 🚀
Tap follow for more AI tips and tools! 🔔
#LocalLLMs #LMStudio #AItools #OpenSourceAI #OnDeviceAI #GPTOSS #AIworkflow #MacBookAir #TechTips #AIVideo
@brendenvogt
34.1K
AKYour LLM worked in the demo.
Then it hit real users… and started hallucinating, looping, or losing context.
That’s not an “LLM problem”.
That’s missing structure.
LangChain is how you connect an LLM to tools, memory, and data.
Prompts, retrievers, APIs, databases — all wired together.
LangGraph is how you control what happens next.
States. Transitions. Loops. Branches.
No more “hope the agent behaves”.
Chain = steps in a line.
Graph = decisions with rules.
If you’re new:
LangChain helps you build faster.
If you’re shipping to prod:
LangGraph helps you keep things sane.
Same rule as backend systems:
Structure beats prompts. Always.
LLMs don’t need more clever prompts.
They need better control flow.
.
.
.
{langchain, langgraph, llm, genai, ai agents, prompt engineering, retrieval augmented generation, agent orchestration, system design, ai engineering}
#genai #llm #aiagents #langchain #langgraph
@akashcode.ai
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