andreaschandra/quora-question-pairs-id
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Andreas Chandra
andreaschandra
AI & ML interests
Sentiment Analysis, Question Answering, Text Summarization, Language Model
Recent Activity
updated
a dataset
about 18 hours ago
andreaschandra/quora-question-pairs-id
published
a dataset
about 18 hours ago
andreaschandra/quora-question-pairs-id
updated
a dataset
1 day ago
andreaschandra/tydiqa-id
Organizations
updated
a
dataset
about 18 hours ago
published
a
dataset
about 18 hours ago
updated
a
dataset
1 day ago
published
a
dataset
1 day ago
updated
a
dataset
2 days ago
reacted to
yeonseok-zeticai's
post with 👀
2 days ago
Post
3065
🚀 Real-Time On-Device AI Agent with Polaris-4B — Run It Yourself, No Cloud, No Cost
We just deployed a real-time on-device AI agent using the Polaris-4B-Preview model — one of the top-performing <6B open LLMs on Hugging Face.
📱 What’s remarkable?
This model runs entirely on a mobile device, without cloud, and without any manual optimization. It was built using ZETIC.MLange, and the best part?
➡️ It’s totally automated, free to use, and anyone can do it.
You don’t need to write deployment code, tweak backends, or touch device-specific SDKs. Just upload your model — and ZETIC.MLange handles the rest.
🧠 About the Model
- Model: Polaris-4B-Preview
- Size: ~4B parameters
- Ranking: Top 3 on Hugging Face LLM Leaderboard (<6B)
- Tokenizer: Token-incremental inference supported
- Modifications: None — stock weights, just optimized for mobile
⚙️ What ZETIC.MLange Does
ZETIC.MLange is a fully automated deployment framework for On-Device AI, built for AI engineers who want to focus on models — not infrastructure.
Here’s what it does in minutes:
- 📊 Analyzes model structure
- ⚙️ Converts to mobile-optimized format (e.g., GGUF, ONNX)
- 📦 Generates a runnable runtime environment with pre/post-processing
- 📱 Targets real mobile hardware (CPU, GPU, NPU — including Qualcomm, MediaTek, Apple)
- 🎯 Gives you a downloadable SDK or mobile app component — ready to run
And yes — this is available now, for free, at https://mlange.zetic.ai
🧪 For AI Engineers Like You, If you want to:
- Test LLMs directly on-device
- Run models offline with no latency
- Avoid cloud GPU costs
- Deploy to mobile without writing app-side inference code
Then this is your moment. You can do exactly what we did, using your own models — all in a few clicks.
🎯 Start here → https://mlange.zetic.ai
📬 Want to try Polaris-4B on your own app? [email protected], or just visit https://mlange.zetic.ai , it is opened as free!
Great work @Chancy , @Zhihui , @tobiaslee !
We just deployed a real-time on-device AI agent using the Polaris-4B-Preview model — one of the top-performing <6B open LLMs on Hugging Face.
📱 What’s remarkable?
This model runs entirely on a mobile device, without cloud, and without any manual optimization. It was built using ZETIC.MLange, and the best part?
➡️ It’s totally automated, free to use, and anyone can do it.
You don’t need to write deployment code, tweak backends, or touch device-specific SDKs. Just upload your model — and ZETIC.MLange handles the rest.
🧠 About the Model
- Model: Polaris-4B-Preview
- Size: ~4B parameters
- Ranking: Top 3 on Hugging Face LLM Leaderboard (<6B)
- Tokenizer: Token-incremental inference supported
- Modifications: None — stock weights, just optimized for mobile
⚙️ What ZETIC.MLange Does
ZETIC.MLange is a fully automated deployment framework for On-Device AI, built for AI engineers who want to focus on models — not infrastructure.
Here’s what it does in minutes:
- 📊 Analyzes model structure
- ⚙️ Converts to mobile-optimized format (e.g., GGUF, ONNX)
- 📦 Generates a runnable runtime environment with pre/post-processing
- 📱 Targets real mobile hardware (CPU, GPU, NPU — including Qualcomm, MediaTek, Apple)
- 🎯 Gives you a downloadable SDK or mobile app component — ready to run
And yes — this is available now, for free, at https://mlange.zetic.ai
🧪 For AI Engineers Like You, If you want to:
- Test LLMs directly on-device
- Run models offline with no latency
- Avoid cloud GPU costs
- Deploy to mobile without writing app-side inference code
Then this is your moment. You can do exactly what we did, using your own models — all in a few clicks.
🎯 Start here → https://mlange.zetic.ai
📬 Want to try Polaris-4B on your own app? [email protected], or just visit https://mlange.zetic.ai , it is opened as free!
Great work @Chancy , @Zhihui , @tobiaslee !
liked
a
model
4 days ago
published
a
model
4 days ago
Quantization
👍
1
7
#1 opened 24 days ago
by
hadadrjt
Sahabat-AI/llama3-8b-cpt-sahabatai-v1-instruct
#2490 opened 22 days ago
by
andreaschandra
reacted to
clem's
post with 🔥
about 1 month ago
Post
6368
Today, we're unveiling two new open-source AI robots! HopeJR for $3,000 & Reachy Mini for $300 🤖🤖🤖
Let's go open-source AI robotics!
Let's go open-source AI robotics!
- 7 replies
updated
a
Space
about 1 month ago
published
a
Space
about 1 month ago
replied to
juhoinkinen's
post
about 2 months ago
Thanks for sharing
reacted to
juhoinkinen's
post with 🚀
about 2 months ago
Post
2775
We (
@osma
,
@MonaLehtinen
& me, i.e. the Annif team at the National Library of Finland) recently took part in the LLMs4Subjects challenge at the SemEval-2025 workshop. The task was to use large language models (LLMs) to generate good quality subject indexing for bibliographic records, i.e. titles and abstracts.
We are glad to report that our system performed well; it was ranked
🥇 1st in the category where the full vocabulary was used
🥈 2nd in the smaller vocabulary category
🏅 4th in the qualitative evaluations.
14 participating teams developed their own solutions for generating subject headings and the output of each system was assessed using both quantitative and qualitative evaluations. Research papers about most of the systems are going to be published around the time of the workshop in late July, and many pre-prints are already available.
We applied Annif together with several LLMs that we used to preprocess the data sets: translated the GND vocabulary terms to English, translated bibliographic records into English and German as required, and generated additional synthetic training data. After the preprocessing, we used the traditional machine learning algorithms in Annif as well as the experimental XTransformer algorithm that is based on language models. We also combined the subject suggestions generated using English and German language records in a novel way.
More information can be found in our system description preprint: Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs (2504.19675)
See also the task description preprint: SemEval-2025 Task 5: LLMs4Subjects -- LLM-based Automated Subject Tagging for a National Technical Library's Open-Access Catalog (2504.07199)
The Annif models trained for this task are available here: NatLibFi/Annif-LLMs4Subjects-data
We are glad to report that our system performed well; it was ranked
🥇 1st in the category where the full vocabulary was used
🥈 2nd in the smaller vocabulary category
🏅 4th in the qualitative evaluations.
14 participating teams developed their own solutions for generating subject headings and the output of each system was assessed using both quantitative and qualitative evaluations. Research papers about most of the systems are going to be published around the time of the workshop in late July, and many pre-prints are already available.
We applied Annif together with several LLMs that we used to preprocess the data sets: translated the GND vocabulary terms to English, translated bibliographic records into English and German as required, and generated additional synthetic training data. After the preprocessing, we used the traditional machine learning algorithms in Annif as well as the experimental XTransformer algorithm that is based on language models. We also combined the subject suggestions generated using English and German language records in a novel way.
More information can be found in our system description preprint: Annif at SemEval-2025 Task 5: Traditional XMTC augmented by LLMs (2504.19675)
See also the task description preprint: SemEval-2025 Task 5: LLMs4Subjects -- LLM-based Automated Subject Tagging for a National Technical Library's Open-Access Catalog (2504.07199)
The Annif models trained for this task are available here: NatLibFi/Annif-LLMs4Subjects-data
-
- 2 replies
reacted to
DawnC's
post with 👀
about 2 months ago
Post
5345
VisionScout — Now with Video Analysis! 🚀
I’m excited to announce a major update to VisionScout, my interactive vision tool that now supports VIDEO PROCESSING, in addition to powerful object detection and scene understanding!
⭐️ NEW: Video Analysis Is Here!
🎬 Upload any video file to detect and track objects using YOLOv8.
⏱️ Customize processing intervals to balance speed and thoroughness.
📊 Get comprehensive statistics and summaries showing object appearances across the entire video.
What else can VisionScout do?
🖼️ Analyze any image and detect 80 object types with YOLOv8.
🔄 Switch between Nano, Medium, and XLarge models for speed or accuracy.
🎯 Filter by object classes (people, vehicles, animals, etc.) to focus on what matters.
📊 View detailed stats on detections, confidence levels, and distributions.
🧠 Understand scenes — interpreting environments and potential activities.
⚠️ Automatically identify possible safety concerns based on detected objects.
What’s coming next?
🔎 Expanding YOLO’s object categories.
⚡ Faster real-time performance.
📱 Improved mobile responsiveness.
My goal:
To bridge the gap between raw detection and meaningful interpretation.
I’m constantly exploring ways to help machines not just "see" but truly understand context — and to make these advanced tools accessible to everyone, regardless of technical background.
Try it now! 🖼️👉 DawnC/VisionScout
If you enjoy VisionScout, a ❤️ Like for this project or feedback would mean a lot and keeps me motivated to keep building and improving!
#ComputerVision #ObjectDetection #VideoAnalysis #YOLO #SceneUnderstanding #MachineLearning #TechForLife
I’m excited to announce a major update to VisionScout, my interactive vision tool that now supports VIDEO PROCESSING, in addition to powerful object detection and scene understanding!
⭐️ NEW: Video Analysis Is Here!
🎬 Upload any video file to detect and track objects using YOLOv8.
⏱️ Customize processing intervals to balance speed and thoroughness.
📊 Get comprehensive statistics and summaries showing object appearances across the entire video.
What else can VisionScout do?
🖼️ Analyze any image and detect 80 object types with YOLOv8.
🔄 Switch between Nano, Medium, and XLarge models for speed or accuracy.
🎯 Filter by object classes (people, vehicles, animals, etc.) to focus on what matters.
📊 View detailed stats on detections, confidence levels, and distributions.
🧠 Understand scenes — interpreting environments and potential activities.
⚠️ Automatically identify possible safety concerns based on detected objects.
What’s coming next?
🔎 Expanding YOLO’s object categories.
⚡ Faster real-time performance.
📱 Improved mobile responsiveness.
My goal:
To bridge the gap between raw detection and meaningful interpretation.
I’m constantly exploring ways to help machines not just "see" but truly understand context — and to make these advanced tools accessible to everyone, regardless of technical background.
Try it now! 🖼️👉 DawnC/VisionScout
If you enjoy VisionScout, a ❤️ Like for this project or feedback would mean a lot and keeps me motivated to keep building and improving!
#ComputerVision #ObjectDetection #VideoAnalysis #YOLO #SceneUnderstanding #MachineLearning #TechForLife
- 2 replies
reacted to
onekq's
post with 🤗
about 2 months ago
Post
3285
This time Gemini is very quick with API support on its 2.5 pro May release. The performance is impressive too, now it is among top contenders like o4, R1, and Claude.
onekq-ai/WebApp1K-models-leaderboard
onekq-ai/WebApp1K-models-leaderboard
updated
a
Space
3 months ago