Share and discover more about AI with social posts from the community.huggingface/OpenAi
🚀 TraVisionLM: Türünün İlk Örneği Türkçe Görsel Dil Modelini Sunuyorum! 🇹🇷🖼
TraVisionLM modelini Hugging Face'te yayınladım! 875M parametre ile bu hafif ve verimli model, görüntüye dayalı Türkçe talimatları işlemek için tasarlandı. Transformers kütüphanesiyle tamamen uyumlu, yüklemesi, eğitmesi ve kullanması çok kolay—dış kütüphane gerekmez!
Tek başıma geliştirdiğim TraVisionLM, düşük kaynaklı dillerde araştırmalar için sağlam bir temel sunuyor. Geliştirmeye devam ederken geri bildirimlerinizi bekliyorum.
🎉 Hemen keşfedin:
- Model:
ucsahin/TraVisionLM-base
- Demo: https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model
- Obje Tespiti İnce Ayarı:
ucsahin/TraVisionLM-Object-Detection-ft
Türkçe görsel dil işleme sınırlarını birlikte zorlayalım!
TraVisionLM modelini Hugging Face'te yayınladım! 875M parametre ile bu hafif ve verimli model, görüntüye dayalı Türkçe talimatları işlemek için tasarlandı. Transformers kütüphanesiyle tamamen uyumlu, yüklemesi, eğitmesi ve kullanması çok kolay—dış kütüphane gerekmez!
Tek başıma geliştirdiğim TraVisionLM, düşük kaynaklı dillerde araştırmalar için sağlam bir temel sunuyor. Geliştirmeye devam ederken geri bildirimlerinizi bekliyorum.
🎉 Hemen keşfedin:
- Model:
ucsahin/TraVisionLM-base
- Demo: https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model
- Obje Tespiti İnce Ayarı:
ucsahin/TraVisionLM-Object-Detection-ft
Türkçe görsel dil işleme sınırlarını birlikte zorlayalım!
huggingface.co
TraVisionLM - Turkish Visual Language Model - a Hugging Face Space by ucsahin
Discover amazing ML apps made by the community
🚀 Introducing TraVisionLM: Turkish Visual Language Model - The First of Its Kind! 🇹🇷🖼
I'm thrilled to share TraVisionLM on Hugging Face! With 875M parameters, this lightweight, efficient model handles Turkish instructions for image inputs. Fully compatible with the Transformers library, it’s easy to load, fine-tune, and use—no external libraries needed!
Developed solo, TraVisionLM is a strong foundation for low-resource language research. While still improving, it's a key step for Turkish-language AI. Your feedback is welcome as I refine the model.
🎉 Explore it now:
- Model:
ucsahin/TraVisionLM-base
- Demo: https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model
- Object Detection Finetune:
ucsahin/TraVisionLM-Object-Detection-ft
Let’s push Turkish visual language processing forward!
I'm thrilled to share TraVisionLM on Hugging Face! With 875M parameters, this lightweight, efficient model handles Turkish instructions for image inputs. Fully compatible with the Transformers library, it’s easy to load, fine-tune, and use—no external libraries needed!
Developed solo, TraVisionLM is a strong foundation for low-resource language research. While still improving, it's a key step for Turkish-language AI. Your feedback is welcome as I refine the model.
🎉 Explore it now:
- Model:
ucsahin/TraVisionLM-base
- Demo: https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model
- Object Detection Finetune:
ucsahin/TraVisionLM-Object-Detection-ft
Let’s push Turkish visual language processing forward!
huggingface.co
TraVisionLM - Turkish Visual Language Model - a Hugging Face Space by ucsahin
Discover amazing ML apps made by the community
Remember when Claude 3.5 Sonnet by @AnthropicAI took the world by storm with Claude Artifacts? 🌍✨
Now we have LlamaCoder, an open-source Claude Artifacts app that can generate full React apps and components with Meta-Llama 3.1 405B. 💻 100% free and open source. 🆓
I like how Llama has now started becoming a placeholder denoting open-source work! 🔓
Originally, Llama was an acronym for Large Language Model Meta AI. 🤖
GitHub: https://github.com/Nutlope/llamacoder
Demo (by togetherAI): https://llamacoder.together.ai
Now we have LlamaCoder, an open-source Claude Artifacts app that can generate full React apps and components with Meta-Llama 3.1 405B. 💻 100% free and open source. 🆓
I like how Llama has now started becoming a placeholder denoting open-source work! 🔓
Originally, Llama was an acronym for Large Language Model Meta AI. 🤖
GitHub: https://github.com/Nutlope/llamacoder
Demo (by togetherAI): https://llamacoder.together.ai
NEW MODEL + DATASET! :)
Check out Enigma, our new code-instruct model:
- trained on synthetic code-instruct data created with Llama 3.1 405b
- high quality code-instruct within the Llama 3.1 Instruct format
The model:
ValiantLabs/Llama3.1-8B-Enigma
The dataset:
sequelbox/Tachibana
Enjoy! We've got more new datasets and models to follow soon.
Check out Enigma, our new code-instruct model:
- trained on synthetic code-instruct data created with Llama 3.1 405b
- high quality code-instruct within the Llama 3.1 Instruct format
The model:
ValiantLabs/Llama3.1-8B-Enigma
The dataset:
sequelbox/Tachibana
Enjoy! We've got more new datasets and models to follow soon.
New sus-column-r model on LMSYS.
https://www.reddit.com/r/LocalLLaMA/comments/1enmcr9/new_suscolumnr_model_on_lmsys_its_just_f_up/
https://www.reddit.com/r/LocalLLaMA/comments/1enmcr9/new_suscolumnr_model_on_lmsys_its_just_f_up/
Databricks ❤️ Hugging Face: up to 40% faster training and tuning of Large Language Models
Generative AI has been taking the world by storm. As the data and AI company, we have been on this journey with the release of the open source large language model Dolly, as well as the internally crowdsourced dataset licensed for research and commercial use that we used to fine-tune it, the databricks-dolly-15k. Both the model and dataset are available on Hugging Face. We’ve learned a lot throughout this process, and today we’re excited to announce our first of many official commits to the Hugging Face codebase that allows users to easily create a Hugging Face Dataset from an Apache Spark™ dataframe.
https://huggingface.co/blog/databricks-case-study
Generative AI has been taking the world by storm. As the data and AI company, we have been on this journey with the release of the open source large language model Dolly, as well as the internally crowdsourced dataset licensed for research and commercial use that we used to fine-tune it, the databricks-dolly-15k. Both the model and dataset are available on Hugging Face. We’ve learned a lot throughout this process, and today we’re excited to announce our first of many official commits to the Hugging Face codebase that allows users to easily create a Hugging Face Dataset from an Apache Spark™ dataframe.
https://huggingface.co/blog/databricks-case-study
huggingface.co
Databricks ❤️ Hugging Face: up to 40% faster training and tuning of Large Language Models
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Rocket Money x Hugging Face: Scaling Volatile ML Models in Production
"We discovered that they were not just service providers, but partners who were invested in our goals and outcomes” - Nicolas Kuzak, Senior ML Engineer at Rocket Money.
Scaling and Maintaining ML Models in Production Without an MLOps Team
We created Rocket Money (a personal finance app formerly known as Truebill) to help users improve their financial wellbeing. Users link their bank accounts to the app which then classifies and categorizes their transactions, identifying recurring patterns to provide a consolidated, comprehensive view of their personal financial life. A critical stage of transaction processing is detecting known merchants and services, some of which Rocket Money can cancel and negotiate the cost of for members. This detection starts with the transformation of short, often truncated and cryptically formatted transaction strings into classes we can use to enrich our product experience.
https://huggingface.co/blog/rocketmoney-case-study
"We discovered that they were not just service providers, but partners who were invested in our goals and outcomes” - Nicolas Kuzak, Senior ML Engineer at Rocket Money.
Scaling and Maintaining ML Models in Production Without an MLOps Team
We created Rocket Money (a personal finance app formerly known as Truebill) to help users improve their financial wellbeing. Users link their bank accounts to the app which then classifies and categorizes their transactions, identifying recurring patterns to provide a consolidated, comprehensive view of their personal financial life. A critical stage of transaction processing is detecting known merchants and services, some of which Rocket Money can cancel and negotiate the cost of for members. This detection starts with the transformation of short, often truncated and cryptically formatted transaction strings into classes we can use to enrich our product experience.
https://huggingface.co/blog/rocketmoney-case-study
huggingface.co
Rocket Money x Hugging Face: Scaling Volatile ML Models in Production
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Fetch Consolidates AI Tools and Saves 30% Development Time with Hugging Face on AWS
xecutive Summary
Fetch, a consumer rewards company, developed about 15 different AI tools to help it receive, route, read, process, analyze, and store receipts uploaded by users. The company has more than 18 million active monthly users for its shopping rewards app. Fetch wanted to rebuild its AI-powered platform and, using Amazon Web Services (AWS) and with the support of AWS Partner Hugging Face, moved from using third-party applications to developing its own tools to gain better insights about customers. Consumers scan receipts —or forward electronic receipts— to receive rewards points for their purchases. Businesses can offer special rewards to users, such as extra points for purchasing a particular product. The company can now process more than 11 million receipts per day faster and gets better data.
https://huggingface.co/blog/fetch-eap-case-study
xecutive Summary
Fetch, a consumer rewards company, developed about 15 different AI tools to help it receive, route, read, process, analyze, and store receipts uploaded by users. The company has more than 18 million active monthly users for its shopping rewards app. Fetch wanted to rebuild its AI-powered platform and, using Amazon Web Services (AWS) and with the support of AWS Partner Hugging Face, moved from using third-party applications to developing its own tools to gain better insights about customers. Consumers scan receipts —or forward electronic receipts— to receive rewards points for their purchases. Businesses can offer special rewards to users, such as extra points for purchasing a particular product. The company can now process more than 11 million receipts per day faster and gets better data.
https://huggingface.co/blog/fetch-eap-case-study
huggingface.co
Fetch Consolidates AI Tools and Saves 30% Development Time with Hugging Face on AWS
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Ryght’s Journey to Empower Healthcare and Life Sciences with Expert Support from Hugging Face
Who is Ryght?
Ryght is building an enterprise-grade generative AI platform tailored for the healthcare and life sciences sectors. Today is their official launch of Ryght Preview, now publicly available for all.
Life science companies are amassing a wealth of data from diverse sources (lab data, EMR, genomics, claims, pharmacy, clinical, etc.), but analysis of that data is archaic, requiring large teams for everything from simple queries to developing useful ML models. There is huge demand for actionable knowledge to drive drug development, clinical trials, and commercial activity, and the rise of precision medicine is only accelerating this demand.
Ryght Laptophttps://huggingface.co/blog/ryght-case-study
Who is Ryght?
Ryght is building an enterprise-grade generative AI platform tailored for the healthcare and life sciences sectors. Today is their official launch of Ryght Preview, now publicly available for all.
Life science companies are amassing a wealth of data from diverse sources (lab data, EMR, genomics, claims, pharmacy, clinical, etc.), but analysis of that data is archaic, requiring large teams for everything from simple queries to developing useful ML models. There is huge demand for actionable knowledge to drive drug development, clinical trials, and commercial activity, and the rise of precision medicine is only accelerating this demand.
Ryght Laptophttps://huggingface.co/blog/ryght-case-study