Venkman42 (Peter Vankman)

liked 3 models 5 days ago

liked 2 models 12 days ago

AIDC-AI/Ovis2-2B

Image-Text-to-Text • Updated 14 days ago • 5.78k • 52

convergence-ai/proxy-lite-3b

Image-Text-to-Text • Updated 4 days ago • 4.29k • 122

reacted to burtenshaw's post with 🔥 about 2 months ago

Post

46794

We’re launching a FREE and CERTIFIED course on Agents!

We're thrilled to announce the launch of the Hugging Face Agents course on Learn! This interactive, certified course will guide you through building and deploying your own AI agents.

Here's what you'll learn:

- Understanding Agents: We'll break down the fundamentals of AI agents, showing you how they use LLMs to perceive their environment (observations), reason about it (thoughts), and take actions. Think of a smart assistant that can book appointments, answer emails, or even write code based on your instructions.
- Building with Frameworks: You'll dive into popular agent frameworks like LangChain, LlamaIndex and smolagents. These tools provide the building blocks for creating complex agent behaviors.
- Real-World Applications: See how agents are used in practice, from automating SQL queries to generating code and summarizing complex documents.
- Certification: Earn a certification by completing the course modules, implementing a use case, and passing a benchmark assessment. This proves your skills in building and deploying AI agents.
Audience

This course is designed for anyone interested in the future of AI. Whether you're a developer, data scientist, or simply curious about AI, this course will equip you with the knowledge and skills to build your own intelligent agents.

Enroll today and start building the next generation of AI agent applications!

https://bit.ly/hf-learn-agents

29 replies

·

liked 3 models 6 months ago

allenai/Molmo-7B-D-0924

Image-Text-to-Text • Updated Oct 10, 2024 • 98.5k • 514

allenai/MolmoE-1B-0924

Image-Text-to-Text • Updated Oct 10, 2024 • 60.2k • 138

gpt-omni/mini-omni

Text-to-Speech • Updated Sep 4, 2024 • 424

New activity in mlabonne/TwinLlama-3.1-8B 6 months ago

Did you continue pretraining with unsloth for this?

#3 opened 6 months ago by

Venkman42

updated a collection 7 months ago

Top Model

Collection

This model outperformed all previous phi-2 based finetunes, except for one MoE implementation • 3 items • Updated Aug 18, 2024 • 2

liked a Space 7 months ago

813

Parler-TTS

🥖

High-fidelity Text-To-Speech

liked a model 8 months ago

qnguyen3/nanoLLaVA-1.5

Image-Text-to-Text • Updated Sep 21, 2024 • 1.2k • 109

liked a Space 8 months ago

124

nanoLLaVA-1.5

🚀

Chat about images with AI

New activity in mlabonne/Yet_Another_LLM_Leaderboard 9 months ago

Nice Leaderboard :)

63

#1 opened about 1 year ago by

Venkman42

reacted to hrishbhdalal's post with 👍 10 months ago

Post

766

I just saw that openai is using an updated tokenizer and it greatly increases the speed of the model and maybe even performance as if we increase the size of vocabulary, it could predict a single token which might be equivalent to two or three tokens in current tokenizers with 50-60k tokens or even 100k. I was thinking of scaling this to a million vocabulary size and then pre training llama3 8b using lora. I know that the model might go to shit, but we can increase the speed of the tokens generation greatly imo. And as one of meta papers said that predicting multiple tokens at the same time can actually increase the performance of a model, so I can imagine increasing the vocabulary in this way means multiple token generation in a way. Yann Lecunn also says that we don’t think in tokens but more like representations or abstractions of situations or problem to be solved. Can scaling to a million vocab size or even 10 million vocab size lead to better and more robust models? Please give me your thoughts on what can go wrong, what can go right etc…