There seems to multiple paid apps shared here that are based on models on hf, but some ppl sell their wrappers as "products" and promote them here. For a long time, hf was the best and only platform to do oss model stuff but with the recent AI website builders anyone can create a product (really crappy ones btw) and try to sell it with no contribution to oss stuff. Please dont do this, or try finetuning the models you use... Sorry for filling yall feed with this bs but yk...
Page : https://huggingface.co/strangerzonehf Describe the artistic properties by posting sample images or links to similar images in the request discussion. If the adapters you're asking for are truly creative and safe for work, I'll train and upload the LoRA to the Stranger Zone repo!
Using a Meta LLaMa checkpoint from Unsloth and some help from the HF community, you can capture handwritten notes and convert them into digital format in just a few second.
Really exciting times for AI builders on Hugging Face.
Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!
### Model Details - **Model Name**: [lettucedect-large-modernbert-en-v1](KRLabsOrg/lettucedect-large-modernbert-en-v1) - **Organization**: [KRLabsOrg](https://huggingface.co/KRLabsOrg) - **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect) - **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens - **Task**: Token Classification / Hallucination Detection - **Training Dataset**: [RagTruth](wandb/RAGTruth-processed) - **Language**: English - **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.
LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.
I've spent most of time working with AI on user-facing apps like Chatbots and TextGen, but today I decided to work on something that I think has a lot of applications for Data Science teams: ZennyKenny/comment_classification
This Space supports uploading a user CSV and categorizing the fields based on user-defined categories. The applications of AI in production are truly endless. π
It's really interesting about the deployment of a new state of matter in Majorana 1: the worldβs first quantum processor powered by topological qubits. If you missed this news this week, here are some links for you:
By default, it performs the following tasks: {Text-to-Text Generation}, {Image-Text-Text Generation} @image: Generates an image using Stable Diffusion xL. @3d: Generates a 3D mesh. @web: Web search agents. @rAgent: Initiates a reasoning chain using Llama mode for coding explanations. @tts1-β, @tts2-β: Voice generation (Female and Male voices). @yolo : Object Detection
Okay this is pretty crazy. Snowflake has CortexAI and Uber is already teasing QueryGPT, both of which prominently feature plain text to SQL features to query your database.
I decided to see how hard it would be to put together something similar using π€ smolagents. Turns out, it was pretty straightforward. I managed to get it done in London Luton airport this afternoon.
I've completed the first unit of the just-launched Hugging Face Agents Course. I would highly recommend it, even for experienced builders, because it is a great walkthrough of the smolagents library and toolkit.
