Top 7 Open-Source LLMs in 2025
1️⃣ DeepSeek R1
An open-source reasoning model excelling in logic, math, and decision-making.
2️⃣ Qwen2.5-72B
Alibaba’s 72B LLM optimized for coding, multilingual tasks, and structured data.
3️⃣ Llama 3.3
Meta’s multilingual LLM with strong dialogue, reasoning, and 128K context.
4️⃣ Mistral-Large
A 123B model excelling in reasoning, coding, and high factual accuracy.
5️⃣ Llama-3.1-70B
A robust instruction-tuned model for research, reasoning, and enterprise use.
6️⃣ Phi-4
Microsoft’s efficient small-scale model for programming and logical reasoning.
7️⃣ Gemma-2-9b-it
Google’s lightweight LLM for reasoning, summarization, and Q&A.
#llm
1️⃣ DeepSeek R1
An open-source reasoning model excelling in logic, math, and decision-making.
2️⃣ Qwen2.5-72B
Alibaba’s 72B LLM optimized for coding, multilingual tasks, and structured data.
3️⃣ Llama 3.3
Meta’s multilingual LLM with strong dialogue, reasoning, and 128K context.
4️⃣ Mistral-Large
A 123B model excelling in reasoning, coding, and high factual accuracy.
5️⃣ Llama-3.1-70B
A robust instruction-tuned model for research, reasoning, and enterprise use.
6️⃣ Phi-4
Microsoft’s efficient small-scale model for programming and logical reasoning.
7️⃣ Gemma-2-9b-it
Google’s lightweight LLM for reasoning, summarization, and Q&A.
#llm
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🗂 A collection of the good Gen AI free courses
🔹 Generative artificial intelligence
1️⃣ Generative AI for Beginners course : building generative artificial intelligence apps.
2️⃣ Generative AI Fundamentals course : getting to know the basic principles of generative artificial intelligence.
3️⃣ Intro to Gen AI course : from learning large language models to understanding the principles of responsible artificial intelligence.
4️⃣ Generative AI with LLMs course : Learn business applications of artificial intelligence with AWS experts in a practical way.
5️⃣ Generative AI for Everyone course : This course tells you what generative artificial intelligence is, how it works, and what uses and limitations it has.
🔹 Generative artificial intelligence
1️⃣ Generative AI for Beginners course : building generative artificial intelligence apps.
2️⃣ Generative AI Fundamentals course : getting to know the basic principles of generative artificial intelligence.
3️⃣ Intro to Gen AI course : from learning large language models to understanding the principles of responsible artificial intelligence.
4️⃣ Generative AI with LLMs course : Learn business applications of artificial intelligence with AWS experts in a practical way.
5️⃣ Generative AI for Everyone course : This course tells you what generative artificial intelligence is, how it works, and what uses and limitations it has.
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⏺️ 7 Free AI Courses for High-Paying Careers
🔹 Build Your First Chatbot Using IBM :- Course Link Click Here
Create AI chatbots with IBM watsonx and NLP basics.
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Learn Deep Learning fundamentals from DeepMind experts.
🔹 Machine Learning Crash Course :- Course Link Click Here
Google's hands-on intro to machine learning.
🔹 Neural networks:- Course Link Click Here
Understand neural networks and their AI applications.
🔹 Applied Machine Learning in Python:- Course Link Click Here
Practical ML techniques using scikit-learn.
🔹 Machine Learning Specialization:- Course Link Click Here
Stanford ML fundamentals course.
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Hands-on computer vision with Python & OpenCV.
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Learn to integrate LangChain, Clerk, Convex, TS & IBM in AI-powered apps. - Video Link
🔹 Build Your First Chatbot Using IBM :- Course Link Click Here
Create AI chatbots with IBM watsonx and NLP basics.
🔹 DeepMind x UCL | Deep Learning :- Course Link Click Here
Learn Deep Learning fundamentals from DeepMind experts.
🔹 Machine Learning Crash Course :- Course Link Click Here
Google's hands-on intro to machine learning.
🔹 Neural networks:- Course Link Click Here
Understand neural networks and their AI applications.
🔹 Applied Machine Learning in Python:- Course Link Click Here
Practical ML techniques using scikit-learn.
🔹 Machine Learning Specialization:- Course Link Click Here
Stanford ML fundamentals course.
🔹 Computer Vision and Image Processing:- Course Link Click Here
Hands-on computer vision with Python & OpenCV.
🎁 Bonus: 🔴 Build an AI Agent in NEXT.JS 15!
Learn to integrate LangChain, Clerk, Convex, TS & IBM in AI-powered apps. - Video Link
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10 Things you need to become an AI/ML engineer:
1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models
1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models
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Data science is a multidisciplinary field that combines techniques from statistics, computer science, and domain-specific knowledge to extract insights and knowledge from data. Here are some essential concepts in data science:
1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs.
2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data.
3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships.
4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding.
5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering.
7. Data Visualization: The graphical representation of data to communicate insights and findings effectively.
8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score.
9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models.
10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis.
These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data.
Join for more: https://t.iss.one/datasciencefun
ENJOY LEARNING 👍👍
1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs.
2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data.
3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships.
4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding.
5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering.
7. Data Visualization: The graphical representation of data to communicate insights and findings effectively.
8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score.
9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models.
10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis.
These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data.
Join for more: https://t.iss.one/datasciencefun
ENJOY LEARNING 👍👍
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🔗 Mistral AI releases Mistral Small 3.1
It outperforms comparable models like Gemma 3 and GPT-4o mini
- It builds upon Mistral Small 3 with improved text performance
- Supports multimodal understanding capabilities
- It has an expanded context window of up to 128k tokens
- Achieves inference speeds of 150 tokens per second
It is described as the first open-source model to surpass leading small proprietary models in text, multimodal, multilingual processing, long-context handling, low latency, and cost efficiency.
It outperforms comparable models like Gemma 3 and GPT-4o mini
- It builds upon Mistral Small 3 with improved text performance
- Supports multimodal understanding capabilities
- It has an expanded context window of up to 128k tokens
- Achieves inference speeds of 150 tokens per second
It is described as the first open-source model to surpass leading small proprietary models in text, multimodal, multilingual processing, long-context handling, low latency, and cost efficiency.
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Prepare for GATE: The Right Time is NOW!
GeeksforGeeks brings you everything you need to crack GATE 2026 – 900+ live hours, 300+ recorded sessions, and expert mentorship to keep you on track.
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GeeksforGeeks brings you everything you need to crack GATE 2026 – 900+ live hours, 300+ recorded sessions, and expert mentorship to keep you on track.
What’s inside?
✔ Live & recorded classes with India’s top educators
✔ 200+ mock tests to track your progress
✔ Study materials - PYQs, workbooks, formula book & more
✔ 1:1 mentorship & AI doubt resolution for instant support
✔ Interview prep for IITs & PSUs to help you land opportunities
Learn from Experts Like:
Satish Kumar Yadav – Trained 20K+ students
Dr. Khaleel – Ph.D. in CS, 29+ years of experience
Chandan Jha – Ex-ISRO, AIR 23 in GATE
Vijay Kumar Agarwal – M.Tech (NIT), 13+ years of experience
Sakshi Singhal – IIT Roorkee, AIR 56 CSIR-NET
Shailendra Singh – GATE 99.24 percentile
Devasane Mallesham – IIT Bombay, 13+ years of experience
Use code UPSKILL30 to get an extra 30% OFF (Limited time only)
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5 Free Courses for Mastering LLMs
1. Introduction to Large Language Models by Google :-
Course Link
2. AI for Educators by Microsoft:- Course Link
3. Cohere’s LLM University:-
Course Link
4. Anthropic Prompt Engineering Courses:-
Course Link
5. Large Language Model Agents:- Course Link
#generativeai
1. Introduction to Large Language Models by Google :-
Course Link
2. AI for Educators by Microsoft:- Course Link
3. Cohere’s LLM University:-
Course Link
4. Anthropic Prompt Engineering Courses:-
Course Link
5. Large Language Model Agents:- Course Link
#generativeai
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Stanford just uploaded their new *"Building LLMS"* lecture. It's a must watch.
These lecture provides a concise overview of building a ChatGPT-like model, covering both pretraining (language modeling) and post-training (SFT/RLHF).
For each component, it explores common practices in data collection, algorithms, and evaluation methods. https://www.youtube.com/watch?v=9vM4p9NN0Ts
These lecture provides a concise overview of building a ChatGPT-like model, covering both pretraining (language modeling) and post-training (SFT/RLHF).
For each component, it explores common practices in data collection, algorithms, and evaluation methods. https://www.youtube.com/watch?v=9vM4p9NN0Ts
YouTube
Stanford CS229 I Machine Learning I Building Large Language Models (LLMs)
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai
This lecture provides a concise overview of building a ChatGPT-like model, covering both pretraining (language modeling) and post-training (SFT/RLHF). For…
This lecture provides a concise overview of building a ChatGPT-like model, covering both pretraining (language modeling) and post-training (SFT/RLHF). For…
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Understanding Generative AI: It's Not AGI
What is Generative AI?
Generative AI refers to algorithms designed to generate new content — from text to images — based on patterns learned from a dataset. Technologies like GPT-4 and DALL-E are popular examples, extensively used for tasks ranging from writing articles to designing graphics.
How Does Generative AI Work?
1 Training: Generative AI models are trained on large datasets, learning the structure, style, and intricacies of the data without human intervention.
2 Pattern Recognition: Through training, these models recognize patterns and correlations in the data, enabling them to predict and generate similar outputs.
3 Output Generation: When provided with a prompt, generative AI uses its training to produce content that aligns with what it has learned, attempting to mimic the input style or respond to the query coherently.
Generative AI vs. AGI:
• Specialization: Generative AI excels in specific tasks it's trained for but lacks the ability to perform beyond its training.
• No Consciousness or Understanding: Unlike AGI, generative AI does not possess consciousness, understanding, or reasoning. It doesn't "think" like humans; it merely processes data based on pre-defined mathematical and probabilistic models.
• Task-Specific: Generative AI operates within the confines of its programming and training, contrasting with AGI's potential to perform any intellectual task that a human can.
Why It Matters:
Understanding the capabilities and limitations of generative AI helps set realistic expectations for its applications. It's a powerful tool for specific tasks but is far from the sci-fi notion of an all-knowing, all-purpose AI.
Generative AI is nowhere near AGI, it even works on different principles. It basically is an average function for non-numerical data. It can create an average text or an average picture from all the texts and pictures it has seen.
What is Generative AI?
Generative AI refers to algorithms designed to generate new content — from text to images — based on patterns learned from a dataset. Technologies like GPT-4 and DALL-E are popular examples, extensively used for tasks ranging from writing articles to designing graphics.
How Does Generative AI Work?
1 Training: Generative AI models are trained on large datasets, learning the structure, style, and intricacies of the data without human intervention.
2 Pattern Recognition: Through training, these models recognize patterns and correlations in the data, enabling them to predict and generate similar outputs.
3 Output Generation: When provided with a prompt, generative AI uses its training to produce content that aligns with what it has learned, attempting to mimic the input style or respond to the query coherently.
Generative AI vs. AGI:
• Specialization: Generative AI excels in specific tasks it's trained for but lacks the ability to perform beyond its training.
• No Consciousness or Understanding: Unlike AGI, generative AI does not possess consciousness, understanding, or reasoning. It doesn't "think" like humans; it merely processes data based on pre-defined mathematical and probabilistic models.
• Task-Specific: Generative AI operates within the confines of its programming and training, contrasting with AGI's potential to perform any intellectual task that a human can.
Why It Matters:
Understanding the capabilities and limitations of generative AI helps set realistic expectations for its applications. It's a powerful tool for specific tasks but is far from the sci-fi notion of an all-knowing, all-purpose AI.
Generative AI is nowhere near AGI, it even works on different principles. It basically is an average function for non-numerical data. It can create an average text or an average picture from all the texts and pictures it has seen.
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