Sara Han
@sdiazlor
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RT @davidberenstei: 🔥 Come and get those AI agents certificates! . Join the cohort of 66K students: .
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RT @davidberenstei: 🔥 @sdiazlor Just published: How to fine-tune Deepseek-R1-Distil-Qwen with synthetic reasoning data!. DeepSeek-R1 has be….
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𝙀𝙭𝙩𝙧𝙖: Stuck on where to start? Just follow our latest tutorial: 𝘍𝘪𝘯𝘦-𝘵𝘶𝘯𝘦 𝘋𝘦𝘦𝘱𝘴𝘦𝘦𝘬-𝘙1 𝘸𝘪𝘵𝘩 𝘢 𝘚𝘺𝘯𝘵𝘩𝘦𝘵𝘪𝘤 𝘙𝘦𝘢𝘴𝘰𝘯𝘪𝘯𝘨 𝘋𝘢𝘵𝘢𝘴𝘦𝘵. Check it here:
huggingface.co
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✨ The Synthetic Data Generator comes with new features:. 🌱 Add seed data to build your chat dataset.🔄 Configure different models for instruction and completion.📁 Save your dataset locally. Test the Space ( or duplicate it!. Check the extra ⤵️.
huggingface.co
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RT @davidberenstei: The RAG's in the bag! . You can now use the Synthetic Data Generator with your own domain-specific seed data to generat….
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💫 Generate RAG data with the Synthetic Data Generator to improve your RAG system!. 1️⃣ Generate from your documents, dataset, or dataset description. 2️⃣ Configure it. 3️⃣ Generate the synthetic dataset. 4️⃣ Fine-tune the retrieval and reranking models. 5️⃣ Build a RAG pipeline.
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🙅♀️ No-code end-to-end example to train your model. 1️⃣ Use the Synthetic Data Generator to create your custom dataset. 2️⃣ Use AutoTrain to use the generated dataset and train your model. Check it here:
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- No code required—everything can be handled through the interface. - 100% free to use. - Designed to create text classification and chat datasets. - Review in Argilla and push to the Hub.
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Where do I get the data from?. We often need to fine-tune models for very specific scenarios. And that’s where the Synthetic Data Generator comes in!. Want to see how it works? Watch this quick video and get started here:
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Pouco a pouco avanzamos! 🚀 Anímovos a contribuír, tan só tedes que entrar na ligazón, ler as instrucións e comezar a anotar ✍.
data-is-better-together-fineweb-c.hf.space
Join and contribute to the dataset glg - galego - Galician
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It only takes 2 steps:.- Coordinate with your Language Lead: Or become one if it is missing: - Read the guidelines and start annotating according to the educational value:
huggingface.co
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Spanish, Filipino, Amharic, French, German, Basque, Catalan, Galician, Guarani, Telugu, Italian, Pashto, Romanian, Tamil, Urdu, Danish. and many more! All included in the FineWeb2 Community Annotation Sprint! 🔥 . 💫 Join to build an impactful dataset for your language!.
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Open Image Preferences released! 🚀 . - Open-source dataset for text2image.- 10K samples manually evaluated by the HF community. - Binarized format for SFT, DPO, or ORPO. It comes with a nice blog post explaining the steps to preprocess and generate the data and the results.
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RT @HamelHusain: Argilla is pretty good now. I didn’t like it before but they’ve made massive improvements and have crossed into genuinely….
argilla.io
Argilla is a collaboration tool for AI engineers and domain experts that strive for data quality, ownership, and efficiency.
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RT @cyrilzakka: Super excited to introduce Halo: A beginner's guide to DIY health tracking with wearables! 🤗✨. Using an $11 smart ring, I'l….
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RT @dvilasuero: Let's go @marqo_ai team!. Here's how to start using their latest open dataset for exploration, labelling, and/or curation w….
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🔍 No bias here: What three key aspects would make an annotation tool ideal for you?. Feel free to share your thoughts! I'm reading you 🤗.
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