Giskard

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https://giskard.ai

giskard_ai

Giskard-AI

AI & ML interests

LLM Agents, Quality, Security, Compliance, Responsible AI, Open Source

giskardai's activity

davidberenstein1957

posted an update 3 days ago

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1878

🚨 New Bonus Unit: Tracing & Evaluating Your Agent! 🚨

Learn how to transform your agent from a simple demo into a robust, reliable product ready for real users.

UNIT: https://huggingface.co/learn/agents-course/bonus-unit2/introduction

In this unit, you'll learn:
- Offline Evaluation – Benchmark and iterate your agent using datasets.
- Online Evaluation – Continuously track key metrics such as latency, costs, and user feedback.

Happy testing and improving!

Thanks Langfuse team!

julien-c

posted an update 17 days ago

Post

2685

Important notice 🚨

For Inference Providers who have built support for our Billing API (currently: Fal, Novita, HF-Inference – with more coming soon), we've started enabling Pay as you go (=PAYG)

What this means is that you can use those Inference Providers beyond the free included credits, and they're charged to your HF account.

You can see it on this view: any provider that does not have a "Billing disabled" badge, is PAYG-compatible.

2 replies

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davidberenstein1957

posted an update 23 days ago

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2384

🔥 Text2SQL, explore and share any data analysis!

🤗 Hugging Face - Dataset Studio is an amazing new feature.

🚀 Start yourself: fka/awesome-chatgpt-prompts

📺 YouTube: https://youtu.be/5LUZq7MHolA?feature=shared

davidberenstein1957

posted an update 24 days ago

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4224

🥊 Epic Agent Framework Showdown! Available today!

🔵 In the blue corner, the versatile challenger with a proven track record of knowledge retrieval: LlamaIndex!

🛑 In the red corner, the defender, weighing in with lightweight efficiency: Hugging Face smolagents!

🔗 URL: https://huggingface.co/agents-course

We just published the LlamaIndex unit for the agents course, and it is set to offer a great contrast between the smolagents unit by looking at

- What makes llama-index stand-out
- How the LlamaHub is used for integrations
- Creating QueryEngine components
- Using agents and tools
- Agentic and multi-agent workflows

The team has been working flat-out on this for a few weeks. Supported by Logan Markewich and Laurie Voss over at LlamaIndex.

Who won? You decide!

davidberenstein1957

posted an update 25 days ago

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3024

🫸 New release to push vector search to the Hub with vicinity and work with any serialisable objects.

🧑‍🏫 KNN, HNSW, USEARCH, ANNOY, PYNNDESCENT, FAISS, and VOYAGER.

🔗 Example Repo: minishlab/my-vicinity-repo

davidberenstein1957

posted an update about 1 month ago

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3297

🚀 Find banger tools for your smolagents!

I created the Tools gallery, which makes tools specifically developed by/for smolagents searchable and visible. This will help with:
- inspiration
- best practices
- finding cool tools

Space: davidberenstein1957/smolagents-and-tools

1 reply

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davidberenstein1957

posted an update about 2 months ago

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2480

Fine-tune Deepseek-R1 with a Synthetic Reasoning Dataset

Blog: https://huggingface.co/blog/sdiazlor/fine-tune-deepseek-with-a-synthetic-reasoning-data

davidberenstein1957

posted an update about 2 months ago

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2076

Agentic RAG: Applied, visual, and step-by-step! 🐾

Get familiar with the Agents and tools, not the bells and whistles!

Retrieve - Augment and now GENERATE.

part 3: https://huggingface.co/blog/davidberenstein1957/ai-blueprint-agentic-rag-part-3-generate

davidberenstein1957

posted an update about 2 months ago

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2881

Anyone can create free hosted tools for their AI agents! 🔥

Agentic RAG stack part 2 - augment
Augment retrieval results by reranking optimises content without increasing time too much

part2: https://huggingface.co/blog/davidberenstein1957/ai-blueprint-agentic-rag-part-2-augment
code: https://github.com/huggingface/ai-blueprint

davidberenstein1957

posted an update about 2 months ago

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2012

Creating an agentic RAG stack on the Hugging Face Hub - part 1 - retrieval (1/5).

🚀 Web apps and microservices included!

Chunk, embed and index documents at a huge scale without overhead.

Blog: https://huggingface.co/blog/davidberenstein1957/ai-blueprint-agentic-rag-part-1-retrieve

davidberenstein1957

posted an update about 2 months ago

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1635

tldr; Parquet is awesome, DuckDB too!

Datasets on the Hugging Face Hub rely on parquet files. We can interact with these files using DuckDB as a fast in-memory database system. One of DuckDB’s features is vector similarity search which can be used with or without an index.

blog:
https://huggingface.co/learn/cookbook/vector_search_with_hub_as_backend

davidberenstein1957

posted an update 2 months ago

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1810

Let's uncover the post-training dataset from DeepSeek-R1 with Magpie!

Pass pre-query tokens <｜begin▁of▁sentence｜>User: , let the model generate the rest.

We can get realistic examples!

Gist: https://gist.github.com/davidberenstein1957/3f20046ce57395a6aba13f8b4e956b59

6 replies

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davidberenstein1957

posted an update 2 months ago

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1893

The RAG's in the bag!

You can now use the Synthetic Data Generator with your own domain-specific seed data to generate a dataset for fine-tuning retrieval or reranking models.

GitHub: https://buff.ly/49IDSmd
Spaces: https://buff.ly/3Y1S99z
Blog: https://huggingface.co/blog/sdiazlor/fine-tune-modernbert-for-rag-with-synthetic-data

1 reply

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davidberenstein1957

posted an update 2 months ago

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You can now use the "Synthetic Data Generator" at a much larger scale with your preferred inference engine: Ollama, vLLM, TGI, and serverless inference! 🔥

Install, configure, launch!

Space: argilla/synthetic-data-generator
Examples: https://github.com/argilla-io/synthetic-data-generator/tree/main/examples

davidberenstein1957

posted an update 2 months ago

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2117

🔦 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

davidberenstein1957

posted an update 3 months ago

Post

1954

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

1 reply

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davidberenstein1957

posted an update 3 months ago

Post

2045

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

davidberenstein1957

posted an update 3 months ago

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1375

🐇 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

davidberenstein1957

posted an update 3 months ago

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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

4 replies

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julien-c

posted an update 4 months ago

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10414

After some heated discussion 🔥, we clarify our intent re. storage limits on the Hub

TL;DR:
- public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible
- private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise)

docs: https://huggingface.co/docs/hub/storage-limits

We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community 🔥

cc: @reach-vb @pierric @victor and the HF team

28 replies

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