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YatharthS 
posted an update 3 days ago
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3591
I just released NovaSR, a tiny 52kb audio upsampler that can enhance 3600 seconds of muffled 16khz audio in to clearer 48khz audio in just 1 second!

NovaSR can
- Enhance TTS model quality.
- Restore poor quality datasets.
- Work on any device(just 52kb which is smaller than a 3 second audio file!)

Model: YatharthS/NovaSR
Space to try it: YatharthS/NovaSR
Github repo: https://github.com/ysharma3501/NovaSR
  • 2 replies
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zc277584121 
posted an update 1 day ago
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1444
We've open-sourced a bilingual Semantic Highlighting model that can power multiple production scenarios:

1) RAG Answer Highlighting — Automatically highlight the exact sentences that answer user queries, improving interpretability and helping users quickly locate relevant information.
2) RAG Noise Filtering — Prune irrelevant context before sending to LLMs, achieving 70-80% token cost reduction while improving answer quality by letting the model focus on what matters.
3) Search System Highlighting — Add semantic highlighting features to recommendation systems, e-commerce search, or any retrieval system where users need to see why a result is relevant.

Try it out: zilliz/semantic-highlight-bilingual-v1
Read our article: https://huggingface.co/blog/zilliz/zilliz-semantic-highlight-model
danielhanchen 
posted an update 1 day ago
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1616
You can now do reinforcement learning training with 7× longer context and no accuracy loss, via our new batching algorithms.

Long reasoning chains in RL are costly, but now we enable you to train gpt-oss with GRPO & reach 380K context on a 192GB GPU.

Blog: https://unsloth.ai/docs/new/grpo-long-context
RakshitAralimatti 
posted an update 2 days ago
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929
I built a crazy ultra–low latency voice assistant agent using Pipecat, NVIDIA Riva, NVIDIA NIM, and an MCP‑powered tool stack. It can talk in real time, search the web, and manage your project directory files, document your code and docs hands‑free (create, read, summarise, and clean up).

Link - https://github.com/rakshit2020/Voice-Agent-using-Nvidia-Riva-NIM-Pipecat
I put everything into a small demo repo with the full architecture diagram and a short demo video so you can see exactly how it works and adapt it to your own projects.

Check out the GitHub, play with the agent, and let me know if it’s useful or if you want a breakdown of any part of the setup.
  • 1 reply
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hypothetical 
posted an update 1 day ago
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1639
We thought it would be easier, but finally we have integrated CuDNN Paged Attention to our models!


Read article here: https://app.thestage.ai/blog/Integrating-cuDNN-Paged-Attention-to-TheStage-AI-Inference?id=8

Llama-8B with CuDNN paged attention, including B200 support: TheStageAI/Elastic-Llama-3.1-8B-Instruct
Mistral-Small-24B with CuDNN paged attention, including B200 support: TheStageAI/Elastic-Mistral-Small-3.1-24B-Instruct-2503
FreshmanD 
posted an update about 18 hours ago
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989
🥇 23 Kaggle Gold Medals. One Agent Framework.

Introducing LoongFlow: A Thinking & Learning Framework for Expert-Grade AI Agents.

Unlike traditional evolve agents(like OpenEvolve-Style), LoongFlow implements the PES (Plan-Execute-Summary) paradigm to learn from mistakes and avoid local optima.

🚀 Highlights:
* SOTA: Surpassed human mathematicians on 11 geometry/algebra problems.
* 23 Kaggle Gold Medals on MLE Bench.
* Efficiency: 60% more efficient than current baselines.

🔗 Code & Paper:
https://github.com/baidu-baige/LoongFlow
LoongFlow: Directed Evolutionary Search via a Cognitive Plan-Execute-Summarize Paradigm (2512.24077)

#AutoML #Kaggle #Agents #OpenSource #LLM
wangbuer999 
posted an update 1 day ago
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2413
HY-MT1.5-1.8B Lightweight Translation Model Open-Source Game-Changer

Tencent raised the bar for lightweight translation!

Supports bidirectional translation across 36 languages total—33 mainstream languages + 5 ethnic/minority dialects

With only 1.8B parameters (less than 1/3 the size of HY-MT1.5-7B), it delivers performance on par with the 7B counterpart and outperforms most commercial translation APIs.

✅ Quantized versions (FP8/GPTQ-Int4) available for edge device deployment, perfect for real-time translation
✅ Full support for terminology intervention, context-aware translation, and formatted output
✅ Ready-to-use prompt templates + seamless integration with Hugging Face Transformers
✅ Recommended transformers ≥ 4.56.0 (FP8 model requires compressed-tensors 0.11.0)

10+ Hugging Face Spaces already integrated this model!

👉 Model Repo: tencent/HY-MT1.5-1.8B
👉 Technical Report: https://arxiv.org/abs/2512.24092
unmodeled-tyler 
posted an update 1 day ago
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951
Hey Hugging Face! I just wanted to share something I've been working on lately. This is Continuum, an app that started as a regular chat interface but quickly spiraled into much more!

The left panel contains settings, different project workspaces with associated chat sessions, and the model drop down menu.

The middle panel is the chat window with engaging color schemes for italics or bold characters.

The right panel is the "Loom" - a collaborative document workspace for the AI model and the user to work together in markdown with a live preview toggle switch.

The Loom supports differential edits allowing the user to reject, approve, or edit each model change/addition. Right now, Continuum will support BYOK, OAI compatible endpoints, and local models served through ollama/llama.cpp

It's still very much a work in progress but I'm really happy with how it's coming along so far. I'm excited to share this demo with all of you when it's ready!
mmhamdy 
posted an update 3 days ago
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2892
The new DeepSeek Engram paper is super fun! It also integrates mHC, and I suspect they're probably releasing all these papers to make the V4 report of reasonable length😄

Here's a nice short summary from Gemini
sergiopaniego 
posted an update 3 days ago
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2781
New REPL environment in OpenEnv available! ✨
Used in the Recursive Language Models (RLM) paper by Alex Zhang.

Ready for inference & post-training using trajectories. Handles long contexts:

> Run Python code in a sandbox
> Make recursive calls to LMs
> Explore data programmatically
> Return final result

Docs: https://meta-pytorch.org/OpenEnv/environments/repl/
Inference script: https://github.com/meta-pytorch/OpenEnv/blob/main/examples/repl_oolong_simple.py
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