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kenza-ily  authored a paper 10 days ago
Machine Translation Hallucination Detection for Low and High Resource Languages using Large Language Models
kenza-ily  authored a paper 10 days ago
Retrieval or Representation? Reassessing Benchmark Gaps in Multilingual and Visually Rich RAG
kenza-ily  authored a paper 11 days ago
How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making?
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Nymbo 
posted an update 5 days ago
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6108
We should really have a release date range slider on the /models page. Tired of "trending/most downloaded" being the best way to sort and still seeing models from 2023 on the first page just because they're embedded in enterprise pipelines and get downloaded repeatedly. "Recently Created/Recently Updated" don't solve the discovery problem considering the amount of noise to sift through.

Slight caveat: Trending actually does have some recency bias, but it's not strong/precise enough.
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peaceAsh 
authored a paper 7 days ago
kenza-ily 
authored 2 papers 10 days ago
kenza-ily 
authored a paper 11 days ago
Reubencf 
posted an update 13 days ago
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2655
🚀 I am thrilled to announce the release of a new Konkani LLM!

We've seen some fantastic results for both translation and transliteration tasks, and I'm excited to share this progress with the community.

📖 Read the launch article and see the results: https://huggingface.co/blog/Reubencf/konkani-llm
🤖 Explore the model and collection:
konkani

I would love to hear your feedback or see what you build with it! #Konkani #LLM #NLP #HuggingFace #IndicNLP #Konkani
tellarin 
authored a paper 23 days ago
Tonic 
posted an update 27 days ago
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3315
🤔 Who would win ?

- a fully subsidized ai lab
OR
- 3 random students named
kurakurai ?

demo : Tonic/fr-on-device

if you like it give the demo a little star and send a shoutout to : @MaxLSB @jddqd and @GAD-cell for absolutely obliterating the pareto frontier of the french language understanding .
  • 4 replies
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Tonic 
posted an update about 1 month ago
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3282
🙋🏻‍♂️hello my lovelies ,

it is with great pleasure i present to you my working one-click deploy 16GB ram completely free huggingface spaces deployment.

repo : Tonic/hugging-claw (use git clone to inspect)
literally the one-click link : Tonic/hugging-claw

you can also run it locally and see for yourself :

docker run -it -p 7860:7860 --platform=linux/amd64 \
-e HF_TOKEN="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_TRUSTED_PROXIES="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_PASSWORD="YOUR_VALUE_HERE" \
-e OPENCLAW_CONTROL_UI_ALLOWED_ORIGINS="YOUR_VALUE_HERE" \
registry.hf.space/tonic-hugging-claw:latest


just a few quite minor details i'll take care of but i wanted to share here first
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Cartinoe5930 
authored a paper about 1 month ago
tellarin 
authored 2 papers about 1 month ago
azminetoushikwasi 
submitted a paper to Daily Papers about 1 month ago
tellarin 
submitted a paper to Daily Papers about 1 month ago
Sri-Vigneshwar-DJ 
posted an update about 1 month ago
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1418
Just released a new dataset designed for training reasoning models on Meta (Facebook/Instagram) advertising fatigue detection!

What is it? A GRPO (Group Relative Policy Optimization) training dataset with 200+ carefully crafted scenarios covering:

🔍 Fatigue Signal Detection: CTR drops, CPM spikes, frequency analysis
🩺 Performance Diagnosis: Root cause analysis frameworks
📋 Strategy: Creative refresh cadence, testing frameworks
📊 Analysis: ROI calculations, metric interpretation
Why GRPO? GRPO training helps models learn structured reasoning. Each response follows the <thinking> and <answer> format.

Check it out here: Sri-Vigneshwar-DJ/meta-fatigue-grpo-dataset
Sri-Vigneshwar-DJ 
posted an update about 2 months ago
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223
🏙️ Hugging Face Community Post
Title: 🧬 Experimenting with "Dynamic Chaos" in Tamil SLMs

Hi everyone! I just published a new experimental study on Small Language Model (SLM) resilience.

I took the Qwen2.5-0.5B model and put it through a "Chaos Phase" to see how much weight data a tiny model can lose before its understanding of classical Tamil grammar breaks.

Key highlights of the study:

Target Data: Fine-tuned on the Thirukkural (1,330 couplets + modern explanations).
The Chaos Step: Applied 20% random weight pruning but implemented "Layer Protection" for the Token Embeddings and LM Head to keep the characters readable.
Compression: 4-bit (Q4_K_M) quantization for extreme efficiency.
Result: A surrealist classical Tamil model that is ultra-light (~300MB) and ultra-fast!

Check out the model and the experiment logic here: Sri-Vigneshwar-DJ/qwen-tamil-chaos-v1
Reubencf 
posted an update about 2 months ago
Reubencf 
posted an update about 2 months ago
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1897
Now Live: The Reubencf/Nano_Banana_Editor now includes 10 free requests/day! 🍌 I'm personally sponsoring these credits to help make open AI accessible to all.
(Note: Limits are subject to change based on funding).

Enjoy !
BounharAbdelaziz 
authored a paper about 2 months ago
BounharAbdelaziz 
submitted a paper to Daily Papers about 2 months ago