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Zero-shot VQA evaluation of Docmatix using LLM - do we need to fine-tune?
While developing Docmatix, we found that fine-tuning Florence-2 performed well on the DocVQA task, but still scored low on the benchmark. To improve the benchmark score, we had to further fine-tune the model on the DocVQA dataset to learn the grammatical style of the benchmark. Interestingly, the human evaluators felt that the additional fine-tuning seemed to perform worse than fine-tuning on Docmatix alone, so we decided to only use the additional fine-tuned model for ablation experiments and publicly release the model fine-tuned on Docmatix alone. Although the answers generated by the model are semantically consistent with the reference answers (as shown in Figure 1), the benchmark scores are low. This raises the question: should we fine-tune the model to improve performance on existing metrics, or should we develop new metrics that are more consistent with human perception?
While developing Docmatix, we found that fine-tuning Florence-2 performed well on the DocVQA task, but still scored low on the benchmark. To improve the benchmark score, we had to further fine-tune the model on the DocVQA dataset to learn the grammatical style of the benchmark. Interestingly, the human evaluators felt that the additional fine-tuning seemed to perform worse than fine-tuning on Docmatix alone, so we decided to only use the additional fine-tuned model for ablation experiments and publicly release the model fine-tuned on Docmatix alone. Although the answers generated by the model are semantically consistent with the reference answers (as shown in Figure 1), the benchmark scores are low. This raises the question: should we fine-tune the model to improve performance on existing metrics, or should we develop new metrics that are more consistent with human perception?
📅 AI Event Scheduler - Streamline event creation with this AI Chrome extension, saving time and reducing manual errors.
📚 Cokeep - Transform bookmarks into collaborative spaces with AI organization, summarization, and team sharing capabilities.
🎨 Crayon AI - Unleash creativity with an all-in-one AI image toolbox, with generation, editing, and optimization for all skill levels.
🖥 Tailwind Genie - Generate responsive UI designs with AI, streamlining web development using Tailwind CSS.
🤗 Video Ai Hug - Transform static photos into personalized hugging videos, bringing cherished moments to life.
📝 Postin - Supercharge your LinkedIn presence with AI-crafted posts, smart management, and engagement-boosting strategies.
📊 Metastory AI v2.2 - Enhance project management with this v2.2 update from Metastory AI that now has Jira integration, project publishing, and an improved editor for streamlined collaboration.
🔎 Beloga - Intelligently capture and seamlessly search across Notion, GDrive, notes, the internet and more simultaneously with a digital brain that’s designed to help amplify your knowledge.
Sick of feeling like a broken record, endlessly repeating instructions?
It’s time to let AI do the talking. Meet Guidde - your GPT-powered ally that transforms even the most complex tasks into crystal-clear, AI-generated video documentation at lightning speed.
Seamlessly share or embed your guides anywhere, hassle-free
Say goodbye to dry documentation and hello to beautiful guides
Reclaim precious time generating documentation 11x faster with AI
Best of all, it only takes 3 steps:
Install the free guidde Chrome extension
Click ‘Capture’ in the extension and ‘Stop’ when done
Sit back and let AI handle the rest, then share your guide
It’s time to let AI do the talking. Meet Guidde - your GPT-powered ally that transforms even the most complex tasks into crystal-clear, AI-generated video documentation at lightning speed.
Seamlessly share or embed your guides anywhere, hassle-free
Say goodbye to dry documentation and hello to beautiful guides
Reclaim precious time generating documentation 11x faster with AI
Best of all, it only takes 3 steps:
Install the free guidde Chrome extension
Click ‘Capture’ in the extension and ‘Stop’ when done
Sit back and let AI handle the rest, then share your guide
🚔 AI Police Cams - Between July and August, AI cameras used in two UK counties detected over 2,000 people not wearing seat belts on three roads, including 109 children. One case involved an unrestrained toddler sitting on a woman's lap in the front passenger seat. Not only are AI-powered cameras being used for seat belts, they’re also being used to catch litterers.
🧠 Qwen - New updates have been made to Qwen’s AI models across multiple modalities. Qwen2-VL is a new vision-language model capable of understanding high-resolution images and 20+ minute videos; Qwen2-Audio is for processing voice inputs; and Qwen-Agent, is an approach to expand 8K context models to handle 1M tokens.
📹 Wyze - A new AI-powered search feature from Wyze allows users to search through their camera footage using keywords and natural language queries. Instead of manually scrolling through recorded events, users can now search for specific objects, people, or activities like "truck," "delivery person," or even more detailed requests like "show me my cat in the backyard."
Celebrating huggingface's acquisition of huggingface.com at a high price.
sequelbox
posted an update
2 days ago
Post
499
new synthetic general chat dataset! meet Supernova, a dataset using prompts from UltraFeedback and responses from Llama 3.1 405b Instruct:
sequelbox/Supernova
new model(s) using the Supernova dataset will follow next week, along with Other Things. (One of these will be a newly updated version of Enigma, utilizing the next version of
sequelbox/Tachibana
with approximately 2x the rows!)
posted an update
2 days ago
Post
499
new synthetic general chat dataset! meet Supernova, a dataset using prompts from UltraFeedback and responses from Llama 3.1 405b Instruct:
sequelbox/Supernova
new model(s) using the Supernova dataset will follow next week, along with Other Things. (One of these will be a newly updated version of Enigma, utilizing the next version of
sequelbox/Tachibana
with approximately 2x the rows!)
just published a demo for Salesforce's new Function Calling Model Salesforce/xLAM
-
Tonic/Salesforce-Xlam-7b-r
-
Tonic/On-Device-Function-Calling
just try em out, and it comes with on-deviceversion too ! cool ! 🚀
-
Tonic/Salesforce-Xlam-7b-r
-
Tonic/On-Device-Function-Calling
just try em out, and it comes with on-deviceversion too ! cool ! 🚀
Estamos tratando de unir, aunar fuerzas y cooperar en experimentos de IA en América Latina. Te invito a unirte a nosotros en «LatinAI». La idea es compartir y organizar espacios, modelos y conjuntos de datos en español/portugués/guaraní/mapuche o ingles para el desarrollo en América Latina.
Siéntete libre de unirte a la organización : https://huggingface.co/LatinAI
---
We are trying to unite, join forces and cooperate in AI experiments in Latin America. We invite you to join us in “LatinAI”. The idea is to share and organize spaces, models and datasets in Spanish/Portuguese/Guarani/Mapuche or English for development in Latin America.
Feel free to join the organization : https://huggingface.co/LatinAI
Siéntete libre de unirte a la organización : https://huggingface.co/LatinAI
---
We are trying to unite, join forces and cooperate in AI experiments in Latin America. We invite you to join us in “LatinAI”. The idea is to share and organize spaces, models and datasets in Spanish/Portuguese/Guarani/Mapuche or English for development in Latin America.
Feel free to join the organization : https://huggingface.co/LatinAI
huggingface.co
LatinAI (AI Developers from Latin America)
Org profile for AI Developers from Latin America on Hugging Face, the AI community building the future.
Just tried LitServe from the good folks at @LightningAI!
Between llama.cpp and vLLM, there is a small gap where a few large models are not deployable!
That's where LitServe comes in!
LitServe is a high-throughput serving engine for AI models built on FastAPI.
Yes, built on FastAPI. That's where the advantage and the issue lie.
It's extremely flexible and supports multi-modality and a variety of models out of the box.
But in my testing, it lags far behind in speed compared to vLLM.
Also, no OpenAI API-compatible endpoint is available as of now.
But as we move to multi-modal models and agents, this serves as a good starting point. However, it’s got to become faster...
GitHub: https://github.com/Lightning-AI/LitServe
Between llama.cpp and vLLM, there is a small gap where a few large models are not deployable!
That's where LitServe comes in!
LitServe is a high-throughput serving engine for AI models built on FastAPI.
Yes, built on FastAPI. That's where the advantage and the issue lie.
It's extremely flexible and supports multi-modality and a variety of models out of the box.
But in my testing, it lags far behind in speed compared to vLLM.
Also, no OpenAI API-compatible endpoint is available as of now.
But as we move to multi-modal models and agents, this serves as a good starting point. However, it’s got to become faster...
GitHub: https://github.com/Lightning-AI/LitServe
GitHub
GitHub - Lightning-AI/LitServe: Lightning-fast serving engine for any AI model of any size. Flexible. Easy. Enterprise-scale.
Lightning-fast serving engine for any AI model of any size. Flexible. Easy. Enterprise-scale. - Lightning-AI/LitServe