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Human Feedback and LLMs
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davidberenstein1957
updated
a
Space
about 3 hours ago
burtenshaw
posted
an
update
about 4 hours ago
Post
1247
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
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
- 2 replies
davidberenstein1957
updated
a
dataset
about 21 hours ago
davidberenstein1957
posted
an
update
1 day ago
Post
1329
🔦 What? The Hub as a vector search backend!
code: https://gist.github.com/davidberenstein1957/f0157a471ec59d9dd44ae6957f1d52ec
build on DuckDB: https://huggingface.co/docs/hub/en/datasets-duckdb
code: https://gist.github.com/davidberenstein1957/f0157a471ec59d9dd44ae6957f1d52ec
build on DuckDB: https://huggingface.co/docs/hub/en/datasets-duckdb
davidberenstein1957
posted
an
update
11 days ago
Post
1920
Fine-tune a SmolLM on domain-specific synthetic data from a LLM
Blog: https://huggingface.co/blog/davidberenstein1957/fine-tune-a-smollm-on-synthetic-data-of-llm
Blog: https://huggingface.co/blog/davidberenstein1957/fine-tune-a-smollm-on-synthetic-data-of-llm
- 1 reply
davidberenstein1957
posted
an
update
16 days ago
Post
1983
Fine-tuning ModernBERT for text classification using synthetic data generation
From prompt to model in 3 steps.
1 dataset description
20 minutes of generating
60 minutes of fine-tuning on my Macbook Pro
Tutorial: https://nbsanity.com/static/552eb50cbd91bedb4e5b73fddca2664a/fine-tune-modernbert-classifier.html
From prompt to model in 3 steps.
1 dataset description
20 minutes of generating
60 minutes of fine-tuning on my Macbook Pro
Tutorial: https://nbsanity.com/static/552eb50cbd91bedb4e5b73fddca2664a/fine-tune-modernbert-classifier.html
burtenshaw
posted
an
update
27 days ago
Post
2699
People are flexing their end of year stats, so I made this app to show hub stats in a tidy design!
Thanks @Ameeeee and @jfcalvo for the feature from Argilla!
burtenshaw/recap
Thanks @Ameeeee and @jfcalvo for the feature from Argilla!
burtenshaw/recap
- 1 reply
davidberenstein1957
posted
an
update
28 days ago
Post
1356
🐇 Tumble down the AI rabbit hole without any technical knowledge!
Explore AI models on the Hub by a simple and quick search
Demo: davidberenstein1957/transformers-pipeline-playground
Explore AI models on the Hub by a simple and quick search
Demo: davidberenstein1957/transformers-pipeline-playground
davidberenstein1957
posted
an
update
about 1 month ago
Post
4193
Introducing the Synthetic Data Generator, a user-friendly application that takes a no-code approach to creating custom datasets with Large Language Models (LLMs). The best part: A simple step-by-step process, making dataset creation a non-technical breeze, allowing anyone to create datasets and models in minutes and without any code.
Blog: https://huggingface.co/blog/synthetic-data-generator
Space: argilla/synthetic-data-generator
Blog: https://huggingface.co/blog/synthetic-data-generator
Space: argilla/synthetic-data-generator
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- 4 replies
burtenshaw
posted
an
update
about 1 month ago
Post
2430
Quick update from week 1 of smol course. The community is taking the driving seat and using the material for their own projects. If you want to do the same, join in!
- we have ongoing translation projects in Korean, Vietnamese, Portuguese, and Spanish
- 3 chapters are ready for students. On topics like, instruction tuning, preference alignment, and parameter efficient fine tuning
- 3 chapters are in progress on evaluation, vision language models, and synthetic data.
- around 780 people have forked the repo to use it for learning, teaching, sharing.
⏭️ Next step is to support people that want to use the course for teaching, content creation, internal knowledge sharing, or anything. If you're into this. Drop an issue or PR
REPO: https://buff.ly/3ZCMKX2
discord channel: https://buff.ly/4f9F8jA
- we have ongoing translation projects in Korean, Vietnamese, Portuguese, and Spanish
- 3 chapters are ready for students. On topics like, instruction tuning, preference alignment, and parameter efficient fine tuning
- 3 chapters are in progress on evaluation, vision language models, and synthetic data.
- around 780 people have forked the repo to use it for learning, teaching, sharing.
⏭️ Next step is to support people that want to use the course for teaching, content creation, internal knowledge sharing, or anything. If you're into this. Drop an issue or PR
REPO: https://buff.ly/3ZCMKX2
discord channel: https://buff.ly/4f9F8jA
davidberenstein1957
posted
an
update
about 1 month ago
Post
2076
Open Preference Dataset for Text-to-Image Generation by the 🤗 Community
Open Image Preferences is an Apache 2.0 licensed dataset for text-to-image generation. This dataset contains 10K text-to-image preference pairs across common image generation categories, while using different model families and varying prompt complexities.
https://huggingface.co/blog/image-preferences
Open Image Preferences is an Apache 2.0 licensed dataset for text-to-image generation. This dataset contains 10K text-to-image preference pairs across common image generation categories, while using different model families and varying prompt complexities.
https://huggingface.co/blog/image-preferences
borgr
authored
9
papers
about 1 month ago
SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation
Paper
•
1805.12386
•
Published
tinyBenchmarks: evaluating LLMs with fewer examples
Paper
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2402.14992
•
Published
•
12
Asymmetry in Low-Rank Adapters of Foundation Models
Paper
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2402.16842
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Published
•
2
Efficient multi-prompt evaluation of LLMs
Paper
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2405.17202
•
Published
•
2
Elements of World Knowledge (EWOK): A cognition-inspired framework for evaluating basic world knowledge in language models
Paper
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2405.09605
•
Published
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
Paper
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2407.00066
•
Published
Learning from Naturally Occurring Feedback
Paper
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2407.10944
•
Published
•
4
Benchmark Agreement Testing Done Right: A Guide for LLM Benchmark Evaluation
Paper
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2407.13696
•
Published
•
5
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
Paper
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2408.07057
•
Published