rag-datasets/rag-mini-bioasq
Viewer • Updated • 44.9k • 2.17k • 37
Juii Kim
watchstep
·
AI & ML interests
None yet
Recent Activity
liked a dataset about 4 hours ago
rag-datasets/rag-mini-bioasq liked a model 26 days ago
Qwen/Qwen3.6-27B liked a model 26 days ago
Qwen/Qwen3-30B-A3BOrganizations
liked a
dataset about 4 hours ago
liked 3
models 26 days ago
liked 2
models 3 months ago
liked a
model 5 months ago
reacted to Kseniase's post with 👍 6 months ago
Post
6539
12 Foundational AI Model Types
Let’s refresh some fundamentals today to stay fluent in the what we all work with. Here are some of the most popular model types that shape the vast world of AI (with examples in the brackets):
1. LLM - Large Language Model (GPT, LLaMA) -> Large Language Models: A Survey (2402.06196)
+ history of LLMs: https://www.turingpost.com/t/The%20History%20of%20LLMs
It's trained on massive text datasets to understand and generate human language. They are mostly build on Transformer architecture, predicting the next token. LLMs scale by increasing overall parameter count across all components (layers, attention heads, MLPs, etc.)
2. SLM - Small Language Model (TinyLLaMA, Phi models, SmolLM) A Survey of Small Language Models (2410.20011)
Lightweight LM optimized for efficiency, low memory use, fast inference, and edge use. SLMs work using the same principles as LLMs
3. VLM - Vision-Language Model (CLIP, Flamingo) -> An Introduction to Vision-Language Modeling (2405.17247)
Processes and understands both images and text. VLMs map images and text into a shared embedding space or generate captions/descriptions from both
4. MLLM - Multimodal Large Language Model (Gemini) -> A Survey on Multimodal Large Language Models (2306.13549)
A large-scale model that can understand and process multiple types of data (modalities) — usually text + other formats, like images, videos, audio, structured data, 3D or spatial inputs. MLLMs can be LLMs extended with modality adapters or trained jointly across vision, text, audio, etc.
5. LAM - Large Action Model (InstructDiffusion, RT-2) -> Large Action Models: From Inception to Implementation (2412.10047)
Understands and generates action sequences by predicting action tokens (discrete/continuous instructions) that guide agents. Trained on behavior datasets, LAMs generalize across tasks, environments, and modalities - video, sensor data, etc.
Read about LRM, MoE, SSM, RNN, CNN, SAM and LNN below👇
Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
Let’s refresh some fundamentals today to stay fluent in the what we all work with. Here are some of the most popular model types that shape the vast world of AI (with examples in the brackets):
1. LLM - Large Language Model (GPT, LLaMA) -> Large Language Models: A Survey (2402.06196)
+ history of LLMs: https://www.turingpost.com/t/The%20History%20of%20LLMs
It's trained on massive text datasets to understand and generate human language. They are mostly build on Transformer architecture, predicting the next token. LLMs scale by increasing overall parameter count across all components (layers, attention heads, MLPs, etc.)
2. SLM - Small Language Model (TinyLLaMA, Phi models, SmolLM) A Survey of Small Language Models (2410.20011)
Lightweight LM optimized for efficiency, low memory use, fast inference, and edge use. SLMs work using the same principles as LLMs
3. VLM - Vision-Language Model (CLIP, Flamingo) -> An Introduction to Vision-Language Modeling (2405.17247)
Processes and understands both images and text. VLMs map images and text into a shared embedding space or generate captions/descriptions from both
4. MLLM - Multimodal Large Language Model (Gemini) -> A Survey on Multimodal Large Language Models (2306.13549)
A large-scale model that can understand and process multiple types of data (modalities) — usually text + other formats, like images, videos, audio, structured data, 3D or spatial inputs. MLLMs can be LLMs extended with modality adapters or trained jointly across vision, text, audio, etc.
5. LAM - Large Action Model (InstructDiffusion, RT-2) -> Large Action Models: From Inception to Implementation (2412.10047)
Understands and generates action sequences by predicting action tokens (discrete/continuous instructions) that guide agents. Trained on behavior datasets, LAMs generalize across tasks, environments, and modalities - video, sensor data, etc.
Read about LRM, MoE, SSM, RNN, CNN, SAM and LNN below👇
Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
- 2 replies
liked a
model 6 months ago
updated a
dataset 9 months ago
published a
dataset 9 months ago
liked 3
datasets 9 months ago
liked a
model 9 months ago
liked a
model 10 months ago
liked 2
datasets 10 months ago
liked a
model 10 months ago
liked a
Space about 1 year ago