YatharthS (Yatharth Sharma)

reacted to their post with 👍 13 days ago

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3490

🤯 🤯 Released a high quality finetuned LLM based TTS model that can generate realistic and clear 48khz audio at over 100x realtime speed! 🤯 🤯

Github link: https://github.com/ysharma3501/MiraTTS

Model link: https://github.com/ysharma3501/MiraTTS

Blog explaining llm tts models: https://huggingface.co/blog/YatharthS/llm-tts-models

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reacted to ZennyKenny's post with 👍 14 days ago

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🍓 One of the coolest parts about being an early Strawberry user has been the opportunity to build on the app at the ground floor.

The platform already has a ton of great integrations that let you interact with your external apps directly with tools, but I wanted to add the ability to do stuff in Slack as well.

💪 So I took the base Anthropic Slack MCP server, added a whole bunch of new tools, and generalized it as an HTTP-based SSE-server and deployed it in like 2 minutes with Railway so that Strawberry could make use of it (as can Claude or any other MCP client).

Now, you can Chat with your Strawberry Companion (or Claude, or whatever) and do things like:
➡️ Get caught up across all of your Slack channels after a long weekend or noisy incident without having to read 20 threads in 10 different channels
➡️ Create, read, and edit Canvases, Messages, and Channels
➡️ Take any resources or content that you're using in your Chat and inject it directly into Slack without copy / paste

😎 I'm pretty pleased with the results, and I made a short demo video showing the results of the work (link in comments). The best part is, it's available on GitHub for anyone else to use too (link in the comments, instructions in the README). The setup takes about 5-10 minutes.

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reacted to rajkumarrawal's post with 👍 14 days ago

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1864

" An open standardized protocol enabling communication for autonomous robots to exchange data, coordinate tasks, and collaborate in real-time environments in the age of AI ". r2r-protocol (Robot2Robot Protocol) is now officially open source! 🔓

"pip install r2r-protocol"

Whether you're a developer, researcher, or tech enthusiast, we invite you to explore, use, and contribute to the project.

🔗 Check it out here: [ https://github.com/Tech-Parivartan/r2r-protocol?tab=readme-ov-file ]

Let’s build the future together! 💡

AiParivartanResearchLab

techparivartan

Documentation of the r2r-protocal : [ https://techparivartanai.notion.site/Robot-to-Robot-r2r-Protocol-1f008f0fb18780439d70e8b9bbbdb869 ]

The R2R Protocol enables seamless robot-to-robot interaction across industrial automation, swarm robotics, logistics, and multi-agent systems. It defines structured message formats, negotiation logic, discovery mechanisms, and extensible APIs.

#r2r_protocol #robot2robot_protocol #ai #aiparivartanresearchlab #techparivartan

https://huggingface.co/blog/rajkumarrawal/rawalraj

reacted to sergiopaniego's post with 🚀 14 days ago

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Google DeepMind releases FunctionGemma, a 240M model specialized in 🔧 tool calling, built for fine-tuning

TRL has day-0 support. To celebrate, we’re sharing 2 new resources:

> Colab guide to fine-tune it for 🌐 browser control with BrowserGym OpenEnv
> Standalone training script

> Colab notebook: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/grpo_functiongemma_browsergym_openenv.ipynb
> Training script: https://github.com/huggingface/trl/blob/main/examples/scripts/openenv/browsergym_llm.py (command to run it inside the script)
> More notebooks in TRL: https://huggingface.co/docs/trl/example_overview#notebooks

reacted to victor's post with 🔥 14 days ago

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Nvidia is on a roll lately. Nemotron 3 Nano is my new fav local model, but here's the real flex: they published the entire evaluation setup. Configs, prompts, logs, all of it. This is how you do open models 🔥

https://huggingface.co/blog/nvidia/nemotron-3-nano-evaluation-recipe

reacted to Nymbo's post with 🔥 14 days ago

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🚨 New tool for the Nymbo/Tools MCP server: The new Agent_Skills tool provides full support for Agent Skills (Claude Skills but open-source).

How it works: The tool exposes the standard discover/info/resources/validate actions. Skills live in /Skills under the same File_System root, and any bundled scripts run through Shell_Command, no new infrastructure required.

Agent_Skills(action="discover")  # List all available skills
Agent_Skills(action="info", skill_name="music-downloader")  # Full SKILL.md
Agent_Skills(action="resources", skill_name="music-downloader")  # Scripts, refs, assets

I've included a music-downloader skill as a working demo, it wraps yt-dlp for YouTube/SoundCloud audio extraction.

Caveat: On HF Spaces, Shell_Command works for most tasks, but some operations (like YouTube downloads) are restricted due to the container environment. For full functionality, run the server locally on your machine.

Try it out ~ https://www.nymbo.net/nymbot

replied to their post 14 days ago

Sorry for the sparse info, I'll add detail later. and Yes it supports voice cloning, just change "reference_file" with your wav/mp3 file path.

reacted to their post with 🚀🔥 15 days ago

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3490

🤯 🤯 Released a high quality finetuned LLM based TTS model that can generate realistic and clear 48khz audio at over 100x realtime speed! 🤯 🤯

Github link: https://github.com/ysharma3501/MiraTTS

Model link: https://github.com/ysharma3501/MiraTTS

Blog explaining llm tts models: https://huggingface.co/blog/YatharthS/llm-tts-models

4 replies

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posted an update 15 days ago

Post

3490

🤯 🤯 Released a high quality finetuned LLM based TTS model that can generate realistic and clear 48khz audio at over 100x realtime speed! 🤯 🤯

Github link: https://github.com/ysharma3501/MiraTTS

Model link: https://github.com/ysharma3501/MiraTTS

Blog explaining llm tts models: https://huggingface.co/blog/YatharthS/llm-tts-models

4 replies

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reacted to their post with 🚀🔥 20 days ago

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I just released LayaCodec, a highly efficient neural audio tokenizer/codec for TTS models, far better than most previous audio tokenizers.

🤯 Next-gen TTS models that use this could achieve several 100s of times real-time speed while producing clearer audio!! 🤯

GitHub repo: https://github.com/ysharma3501/LayaCodec
Model: YatharthS/LayaCodec

posted an update 20 days ago

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2824

I just released LayaCodec, a highly efficient neural audio tokenizer/codec for TTS models, far better than most previous audio tokenizers.

🤯 Next-gen TTS models that use this could achieve several 100s of times real-time speed while producing clearer audio!! 🤯

GitHub repo: https://github.com/ysharma3501/LayaCodec
Model: YatharthS/LayaCodec

reacted to tomaarsen's post with 🔥 21 days ago

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🐦‍🔥 I've just published Sentence Transformers v5.2.0! It introduces multi-processing for CrossEncoder (rerankers), multilingual NanoBEIR evaluators, similarity score outputs in mine_hard_negatives, Transformers v5 support and more. Details:

- CrossEncoder multi-processing: Similar to SentenceTransformer and SparseEncoder, you can now use multi-processing with CrossEncoder rerankers. Useful for multi-GPU and CPU settings, and simple to configure: just device=["cuda:0", "cuda:1"] or device=["cpu"]*4 on the model.predict or model.rank calls.

- Multilingual NanoBEIR Support: You can now use community translations of the tiny NanoBEIR retrieval benchmark instead of only the English one, by passing dataset_id, e.g. dataset_id="lightonai/NanoBEIR-de" for the German benchmark.

- Similarity scores in Hard Negatives Mining: When mining for hard negatives to create a strong training dataset, you can now pass output_scores=True to get similarity scores returned. This can be useful for some distillation losses!

- Transformers v5: This release works with both Transformers v4 and the upcoming v5. In the future, Sentence Transformers will only work with Transformers v5, but not yet!

- Python 3.9 deprecation: Now that Python 3.9 has lost security support, Sentence Transformers no longer supports it.

Check out the full changelog for more details: https://github.com/huggingface/sentence-transformers/releases/tag/v5.2.0

I'm quite excited about what's coming. There's a huge draft PR with a notable refactor in the works that should bring some exciting support. Specifically, better multimodality, rerankers, and perhaps some late interaction in the future!

reacted to their post with 🔥 about 1 month ago

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Just uploaded a detailed blog about my findings in optimizing NeuTTS to generate 200 seconds of audio in a single second. Also went in depth in NeuTTS’s architecture. Will be happy to answer any questions.

https://huggingface.co/blog/YatharthS/making-neutts-200x-realtime

reacted to hiyouga's post with 🔥 about 1 month ago

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🚀 We're excited to support the ERNIE AI Developer Challenge!

Fine-tune ERNIE with LLaMA-Factory and compete for $3,000 prizes by building the most impactful model — with submissions reviewed by the core developers of LLaMA-Factory.

👉 Join Now: https://baiduernieai.devpost.com/?utm_source=LLaMAFactory&utm_medium=partner&utm_campaign=ERNIE+AI+Developer+Challenge

reacted to rajkumarrawal's post with 👀 about 1 month ago

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Annual AI Investment ROI Calculator:

Current Manual Process Cost: $X
AI Implementation Cost: $Y
Expected Efficiency Gain: Z%

Annual Savings = X × Z% = A
Annual AI Cost = Y
Net Annual Benefit = A - Y = B

ROI = (B ÷ Y) × 100%
Payback Period = (Y ÷ B) * 12 months

Monthly LLM's Intelligence Reports for AI Decision Makers :

Repository link is in comments below :

AiParivartanResearchLab

1 reply

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reacted to onekq's post with 👍 about 1 month ago

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794

Sorry folks. No SOTA from GPT 5.1

onekq-ai/WebApp1K-models-leaderboard

reacted to samerzaher80's post with 🚀 about 1 month ago

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New Research Release: AetherMind_SRL — Self-Reflective Learning for Robust Natural Language Inference (NLI)
By: Sameer S. Najm

After more than a year of continuous work, experimentation, and refinement, I am proud to officially publish AetherMind_SRL, a self-improving Transformer model trained using Self-Reflective Learning (SRL) — a technique that enables the model to learn from its own mistakes.

This research integrates:

🔹 Knowledge Distillation from DeBERTa-v3-base
🔹 Self-Reflective Learning loops
🔹 Adversarial ANLI training
🔹 Clinical Alzheimer’s reasoning (ADNI)
🔹 SMART Error Buffers for hard-example mining

What makes it unique?
AetherMind_SRL continuously improves through structured error logs, balanced correction buffers, and clinical-domain adaptation. It achieves strong performance across general NLI benchmarks, adversarial datasets, and Alzheimer’s-focused reasoning tasks.

Official Research Preprint (Zenodo DOI):
https://doi.org/10.5281/zenodo.17670971
Model Repository (Hugging Face):
samerzaher80/AetherMind_SRL
Full community article (Hugging Face Blog):
https://huggingface.co/blog/samerzaher80/aethermind-srl

reacted to mike-ravkine's post with 👀 about 1 month ago

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Applying Hazard and Entropy Analysis to LLMs

Here's an example of a model that behaves perfectly well up to 8k, smoothly increasing its entropy before going into a struggle zone, collapsing, seeing a region of recovery and finally falling down hard at the 16k wall.

Is your model implementation behaving badly like this?

Would you know if it was? 👀

Yatharth Sharma

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Yatharth Sharma

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