rajkumarrawal (Rajkumar rawal)

reacted to YatharthS's post with 👍 6 days ago

Post

3364

🤯 🤯 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 danielhanchen's post with 👍 6 days ago

Post

5170

NVIDIA releases Nemotron 3 Nano, a new 30B hybrid reasoning model! 🔥

Has 1M context window & best in class performance for SWE-Bench, reasoning & chat. Run the MoE model locally with 24GB RAM.

GGUF: unsloth/Nemotron-3-Nano-30B-A3B-GGUF
💚 Step-by-step Guide: https://docs.unsloth.ai/models/nemotron-3

1 reply

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reacted to danielhanchen's post with 👍 6 days ago

Post

2100

Google releases FunctionGemma, a new 270M parameter model that runs on just 0.5 GB RAM.✨

Built for tool-calling, run locally on your phone at 50+ tokens/s, or fine-tune with Unsloth & deploy to your phone.

GGUF: unsloth/functiongemma-270m-it-GGUF
Docs + Notebook: https://docs.unsloth.ai/models/functiongemma

2 replies

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reacted to their post with 👍 7 days ago

Post

1835

" 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

posted an update 7 days ago

Post

1835

" 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

replied to their post 18 days ago

https://github.com/rawalraj022/aiprl-llm-intelligence-report/blob/main/2025_AD_Top_LLM_Benchmark_Evaluations/9)September(2025)/Core_Knowledge_%26_Reasoning_Benchmarks/Core_Knowledge_%26_Reasoning_Benchmarks.pdf

replied to their post 18 days ago

Research Highlights :
Comparative insights, evaluation methodologies, and industry trends for AI decision makers.

Disclaimer:
This comprehensive Core Knowledge & Reasoning analysis represents the current state of large language model capabilities as of September 2025. All performance metrics are based on standardized evaluations and may vary based on specific implementation details, hardware configurations, and testing methodologies. Users are advised to consult original research papers and official documentation for detailed technical insights and application guidelines. Individual model performance may differ in real-world scenarios and should be validated accordingly. If there are any discrepancies or updates beyond this report, please refer to the respective model providers for the most current information.

reacted to their post with 👍 18 days ago

Post

2813

September(2025) LLM Core Knowledge & Reasoning Benchmarks Report By

AiParivartanResearchLab (AIPRL-LIR)

Monthly LLM's Intelligence Reports for AI Decision Makers :
Our "aiprl-llm-intelligence-report" repo to establishes (AIPRL-LIR) framework for Large Language Model overall evaluation and analysis through systematic monthly intelligence reports. Unlike typical AI research papers or commercial reports. It provides structured insights into AI model performance, benchmarking methodologies, Multi-hosting provider analysis, industry trends ...

( all in one monthly report ) Leading Models & Companies, 23 Benchmarks in 6 Categories, Global Hosting Providers, & Research Highlights

Here’s what you’ll find inside this month’s intelligence report:-

Leading Models & Companies :

openai ,

Anthropic ,

meta-llama ,

deepmind

google ,

mistralai ,

Cohere ,

Qwen ,

deepseek-ai ,

MicrosoftResearch ,

amazonwebservices ,

nvidia ,

grokgpt-org and more.

23 Benchmarks in 6 Categories :
With a special focus on Core Knowledge & Reasoning performance across diverse tasks.

Repository link is in comments below :

https://huggingface.co/blog/rajkumarrawal/september-2025-aiprl-lir-core-knowledge-reasoning#6935ab96da60512619b7cf1f

3 replies

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posted an update 18 days ago

Post

2813

September(2025) LLM Core Knowledge & Reasoning Benchmarks Report By

AiParivartanResearchLab (AIPRL-LIR)

Monthly LLM's Intelligence Reports for AI Decision Makers :
Our "aiprl-llm-intelligence-report" repo to establishes (AIPRL-LIR) framework for Large Language Model overall evaluation and analysis through systematic monthly intelligence reports. Unlike typical AI research papers or commercial reports. It provides structured insights into AI model performance, benchmarking methodologies, Multi-hosting provider analysis, industry trends ...

( all in one monthly report ) Leading Models & Companies, 23 Benchmarks in 6 Categories, Global Hosting Providers, & Research Highlights

Here’s what you’ll find inside this month’s intelligence report:-

Leading Models & Companies :

openai ,

Anthropic ,

meta-llama ,

deepmind

google ,

mistralai ,

Cohere ,

Qwen ,

deepseek-ai ,

MicrosoftResearch ,

amazonwebservices ,

nvidia ,

grokgpt-org and more.

23 Benchmarks in 6 Categories :
With a special focus on Core Knowledge & Reasoning performance across diverse tasks.

Repository link is in comments below :

https://huggingface.co/blog/rajkumarrawal/september-2025-aiprl-lir-core-knowledge-reasoning#6935ab96da60512619b7cf1f

3 replies

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reacted to their post with 👍 21 days ago

Post

1129

September(2025) LLM Commonsense & Social Benchmarks Report By

AiParivartanResearchLab (AIPRL-LIR)

Monthly LLM's Intelligence Reports for AI Decision Makers :
Our "aiprl-llm-intelligence-report" repo to establishes (AIPRL-LIR) framework for Large Language Model overall evaluation and analysis through systematic monthly intelligence reports. Unlike typical AI research papers or commercial reports. It provides structured insights into AI model performance, benchmarking methodologies, Multi-hosting provider analysis, industry trends ...

( all in one monthly report ) Leading Models & Companies, 23 Benchmarks in 6 Categories, Global Hosting Providers, & Research Highlights

Here’s what you’ll find inside this month’s intelligence report:-

Leading Models & Companies :

openai ,

Anthropic ,

meta-llama ,

google

deepmind ,

mistralai ,

Cohere ,

Qwen ,

deepseek-ai ,

MicrosoftResearch ,

amazonwebservices ,

nvidia ,

grokgpt-org and more.

23 Benchmarks in 6 Categories :
With a special focus on Commonsense & Social performance across diverse tasks.

Repository link is in comments below :

https://huggingface.co/blog/rajkumarrawal/september-2025-aiprl-lir-commonsense-social

2 replies

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replied to their post 21 days ago

https://github.com/rawalraj022/aiprl-llm-intelligence-report/blob/main/2025_AD_Top_LLM_Benchmark_Evaluations/9)September(2025)/Commonsense_%26_Social_Benchmarks/Commonsense_%26_Social_Benchmarks.pdf

replied to their post 21 days ago

Research Highlights :
Comparative insights, evaluation methodologies, and industry trends for AI decision makers.

Disclaimer:
This comprehensive Commonsense & Social analysis represents the current state of large language model capabilities as of September 2025. All performance metrics are based on standardized evaluations and may vary based on specific implementation details, hardware configurations, and testing methodologies. Users are advised to consult original research papers and official documentation for detailed technical insights and application guidelines. Individual model performance may differ in real-world scenarios and should be validated accordingly. If there are any discrepancies or updates beyond this report, please refer to the respective model providers for the most current information.

posted an update 21 days ago

Post

1129

September(2025) LLM Commonsense & Social Benchmarks Report By

AiParivartanResearchLab (AIPRL-LIR)

Monthly LLM's Intelligence Reports for AI Decision Makers :
Our "aiprl-llm-intelligence-report" repo to establishes (AIPRL-LIR) framework for Large Language Model overall evaluation and analysis through systematic monthly intelligence reports. Unlike typical AI research papers or commercial reports. It provides structured insights into AI model performance, benchmarking methodologies, Multi-hosting provider analysis, industry trends ...

( all in one monthly report ) Leading Models & Companies, 23 Benchmarks in 6 Categories, Global Hosting Providers, & Research Highlights

Here’s what you’ll find inside this month’s intelligence report:-

Leading Models & Companies :

openai ,

Anthropic ,

meta-llama ,

google

deepmind ,

mistralai ,

Cohere ,

Qwen ,

deepseek-ai ,

MicrosoftResearch ,

amazonwebservices ,

nvidia ,

grokgpt-org and more.

23 Benchmarks in 6 Categories :
With a special focus on Commonsense & Social performance across diverse tasks.

Repository link is in comments below :

https://huggingface.co/blog/rajkumarrawal/september-2025-aiprl-lir-commonsense-social

2 replies

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reacted to their post with 👍 22 days ago

Post

2490

September(2025) LLM Safety & Reliability Benchmarks Report By AI Parivartan Research Lab (AIPRL-LIR)

Monthly LLM's Intelligence Reports for AI Decision Makers :

Our "aiprl-llm-intelligence-report" repo to establishes (AIPRL-LIR) framework for Large Language Model overall evaluation and analysis through systematic monthly intelligence reports. Unlike typical AI research papers or commercial reports. It provides structured insights into AI model performance, benchmarking methodologies, Multi-hosting provider analysis, industry trends ...

( all in one monthly report ) Leading Models & Companies, 23 Benchmarks in 6 Categories, Global Hosting Providers, & Research Highlights

Here’s what you’ll find inside this month’s intelligence report:-

Leading Models & Companies :

23 Benchmarks in 6 Categories :
With a special focus on Safety & Reliability performance across diverse tasks.

Global Hosting Providers :

Research Highlights :
Comparative insights, evaluation methodologies, and industry trends for AI decision makers.

Disclaimer:
This comprehensive Safety & Reliability analysis represents the current state of large language model capabilities as of September 2025. All performance metrics are based on standardized evaluations and may vary based on specific implementation details, hardware configurations, and testing methodologies. Users are advised to consult original research papers and official documentation for detailed technical insights and application guidelines. Individual model performance may differ in real-world scenarios and should be validated accordingly. If there are any discrepancies or updates beyond this report, please refer to the respective model providers for the most current information.

Repository link is in comments below :

https://huggingface.co/blog/rajkumarrawal/september-2025-aiprl-lir-safety-reliability

AiParivartanResearchLab

1 reply

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reacted to their post with 👍 23 days ago

Post

302

The best way to predict the future is to evaluate it systematically.

AiParivartanResearchLab

reacted to their post with 👍 23 days ago

Post

840

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

·

replied to their post 23 days ago

https://github.com/rawalraj022/aiprl-llm-intelligence-report/blob/main/2025_AD_Top_LLM_Benchmark_Evaluations/9)September(2025)/Safety_%26_Reliability_Benchmarks/Safety_%26_Reliability_Benchmarks.pdf

posted an update 23 days ago

Post

2490

September(2025) LLM Safety & Reliability Benchmarks Report By AI Parivartan Research Lab (AIPRL-LIR)

Monthly LLM's Intelligence Reports for AI Decision Makers :

Our "aiprl-llm-intelligence-report" repo to establishes (AIPRL-LIR) framework for Large Language Model overall evaluation and analysis through systematic monthly intelligence reports. Unlike typical AI research papers or commercial reports. It provides structured insights into AI model performance, benchmarking methodologies, Multi-hosting provider analysis, industry trends ...

( all in one monthly report ) Leading Models & Companies, 23 Benchmarks in 6 Categories, Global Hosting Providers, & Research Highlights

Here’s what you’ll find inside this month’s intelligence report:-

Leading Models & Companies :

23 Benchmarks in 6 Categories :
With a special focus on Safety & Reliability performance across diverse tasks.

Global Hosting Providers :

Research Highlights :
Comparative insights, evaluation methodologies, and industry trends for AI decision makers.

Disclaimer:
This comprehensive Safety & Reliability analysis represents the current state of large language model capabilities as of September 2025. All performance metrics are based on standardized evaluations and may vary based on specific implementation details, hardware configurations, and testing methodologies. Users are advised to consult original research papers and official documentation for detailed technical insights and application guidelines. Individual model performance may differ in real-world scenarios and should be validated accordingly. If there are any discrepancies or updates beyond this report, please refer to the respective model providers for the most current information.

Repository link is in comments below :

https://huggingface.co/blog/rajkumarrawal/september-2025-aiprl-lir-safety-reliability

AiParivartanResearchLab

1 reply

·

replied to their post about 1 month ago

https://github.com/rawalraj022/aiprl-llm-intelligence-report

posted an update about 1 month ago

Post

840

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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