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Say hello to
We are glad to announce a long-awaited quality-of-life improvement: the Hugging Face CLI has been officially renamed from huggingface-cli to hf!
So... why this change?
Typing huggingface-cli constantly gets old fast. More importantly, the CLI’s command structure became messy as new features were added over time (upload, download, cache management, repo management, etc.). Renaming the CLI is a chance to reorganize commands into a clearer, more consistent format.
We decided not to reinvent the wheel and instead follow a well-known CLI pattern: hf <resource> <action>. Isn't
The full rationale, implementation details, and migration notes are in the blog post: https://huggingface.co/blog/hf-cli
hf: a faster, friendlier Hugging Face CLI ✨We are glad to announce a long-awaited quality-of-life improvement: the Hugging Face CLI has been officially renamed from huggingface-cli to hf!
So... why this change?
Typing huggingface-cli constantly gets old fast. More importantly, the CLI’s command structure became messy as new features were added over time (upload, download, cache management, repo management, etc.). Renaming the CLI is a chance to reorganize commands into a clearer, more consistent format.
We decided not to reinvent the wheel and instead follow a well-known CLI pattern: hf <resource> <action>. Isn't
hf auth login easier to type and remember?The full rationale, implementation details, and migration notes are in the blog post: https://huggingface.co/blog/hf-cli
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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
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