Thanks @treehugg3 for the thoughtful feedback! 🙏 My main motive here was to break it down in simple words for people who are new to AI or just starting to learn about reasoning models. I completely understand your concern I’ll make sure to include more detailed explanations, examples, sources, and technical depth (like backtracking and novel reasoning paths) in upcoming blogs. Really appreciate your input, it helps me improve!
Rakshit Aralimatti
RakshitAralimatti
·
AI & ML interests
Nvidia
Recent Activity
replied to
their
post
6 days ago
When you ask ChatGPT, Claude, or Gemini a really tough question,
you might notice that little "thinking..." moment before it answers.
But what does it actually mean when an LLM is “thinking”?
Imagine a chess player pausing before their next move not because they don’t know how to play, but because they’re running through possibilities, weighing options, and choosing the best one.
LLMs do something similar… except they’re not really thinking like us.
Here’s the surprising part :-
You might think these reasoning skills come from futuristic architectures or alien neural networks.
In reality, most reasoning LLMs still use the same transformer decoder-only architecture as other models
The real magic?
It’s in how they’re trained and what data they learn from.
Can AI actually think, or is it just insanely good at faking it?
I broke it down in a simple, 4-minute Medium read.
Bet you’ll walk away with at least one “aha!” moment. 🚀
Read here - https://lnkd.in/edZ8Ceyg
replied to
their
post
6 days ago
When you ask ChatGPT, Claude, or Gemini a really tough question,
you might notice that little "thinking..." moment before it answers.
But what does it actually mean when an LLM is “thinking”?
Imagine a chess player pausing before their next move not because they don’t know how to play, but because they’re running through possibilities, weighing options, and choosing the best one.
LLMs do something similar… except they’re not really thinking like us.
Here’s the surprising part :-
You might think these reasoning skills come from futuristic architectures or alien neural networks.
In reality, most reasoning LLMs still use the same transformer decoder-only architecture as other models
The real magic?
It’s in how they’re trained and what data they learn from.
Can AI actually think, or is it just insanely good at faking it?
I broke it down in a simple, 4-minute Medium read.
Bet you’ll walk away with at least one “aha!” moment. 🚀
Read here - https://lnkd.in/edZ8Ceyg
commented on
their
article
7 days ago
What’s MXFP4? The 4-Bit Secret Powering OpenAI’s GPT‑OSS Models on Modest Hardware
Organizations
replied to
their
post
6 days ago
replied to
their
post
6 days ago
Thanks @Edalexan .Glad that you found it useful.
commented on
What’s MXFP4? The 4-Bit Secret Powering OpenAI’s GPT‑OSS Models on Modest Hardware
7 days ago
Hi
@itschandra09
, the E2M1 structure is 1 sign bit, 2 exponent bits, and 1 mantissa bit.
sorry for wrong image.
posted
an
update
7 days ago
Post
6709
When you ask ChatGPT, Claude, or Gemini a really tough question,
you might notice that little "thinking..." moment before it answers.
But what does it actually mean when an LLM is “thinking”?
Imagine a chess player pausing before their next move not because they don’t know how to play, but because they’re running through possibilities, weighing options, and choosing the best one.
LLMs do something similar… except they’re not really thinking like us.
Here’s the surprising part :-
You might think these reasoning skills come from futuristic architectures or alien neural networks.
In reality, most reasoning LLMs still use the same transformer decoder-only architecture as other models
The real magic?
It’s in how they’re trained and what data they learn from.
Can AI actually think, or is it just insanely good at faking it?
I broke it down in a simple, 4-minute Medium read.
Bet you’ll walk away with at least one “aha!” moment. 🚀
Read here - https://lnkd.in/edZ8Ceyg
you might notice that little "thinking..." moment before it answers.
But what does it actually mean when an LLM is “thinking”?
Imagine a chess player pausing before their next move not because they don’t know how to play, but because they’re running through possibilities, weighing options, and choosing the best one.
LLMs do something similar… except they’re not really thinking like us.
Here’s the surprising part :-
You might think these reasoning skills come from futuristic architectures or alien neural networks.
In reality, most reasoning LLMs still use the same transformer decoder-only architecture as other models
The real magic?
It’s in how they’re trained and what data they learn from.
Can AI actually think, or is it just insanely good at faking it?
I broke it down in a simple, 4-minute Medium read.
Bet you’ll walk away with at least one “aha!” moment. 🚀
Read here - https://lnkd.in/edZ8Ceyg
-
- 9 replies
commented on
What’s MXFP4? The 4-Bit Secret Powering OpenAI’s GPT‑OSS Models on Modest Hardware
11 days ago
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upvoted
an
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12 days ago
Article
What’s MXFP4? The 4-Bit Secret Powering OpenAI’s GPT‑OSS Models on Modest Hardware
By
•
•
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published
an
article
16 days ago
Article
What’s MXFP4? The 4-Bit Secret Powering OpenAI’s GPT‑OSS Models on Modest Hardware
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an
update
16 days ago
Post
239
🤔 Ever wondered how OpenAI’s massive GPT‑OSS‑20B runs on just 16 GB of memory or how GPT‑OSS‑120B runs on a single H100 GPU?
Seems impossible, right?
The secret is Native MXFP4 Quantization it's a 4-bit floating-point format that’s making AI models faster, lighter, and more deployable than ever.
🧠 What’s MXFP4?
MXFP4, or Microscaling FP4, is a specialized 4-bit floating‑point format (E2M1) standardized by the Open Compute Project under the MX (Microscaling) specification. It compresses groups of 32 values using a shared 8-bit scale (E8M0), dramatically lowering memory usage while preserving the dynamic range perfect for compact AI model deployment.
💡 Think of it like this:
Instead of everyone ordering their own expensive meal (full-precision weights), a group shares a family meal (shared scaling). It’s cheaper, lighter, and still gets the job done.
✍️ I’ve broken all of this down in my first Medium blog:
What’s MXFP4? The 4-Bit Secret Powering OpenAI’s GPT‑OSS Models on Modest Hardware
Link - https://medium.com/@rakshitaralimatti2001/4-bit-alchemy-how-mxfp4-makes-massive-models-like-gpt-oss-feasible-for-everyone-573d6630b56c
HF - https://huggingface.co/blog/RakshitAralimatti/learn-ai-with-me
Seems impossible, right?
The secret is Native MXFP4 Quantization it's a 4-bit floating-point format that’s making AI models faster, lighter, and more deployable than ever.
🧠 What’s MXFP4?
MXFP4, or Microscaling FP4, is a specialized 4-bit floating‑point format (E2M1) standardized by the Open Compute Project under the MX (Microscaling) specification. It compresses groups of 32 values using a shared 8-bit scale (E8M0), dramatically lowering memory usage while preserving the dynamic range perfect for compact AI model deployment.
💡 Think of it like this:
Instead of everyone ordering their own expensive meal (full-precision weights), a group shares a family meal (shared scaling). It’s cheaper, lighter, and still gets the job done.
✍️ I’ve broken all of this down in my first Medium blog:
What’s MXFP4? The 4-Bit Secret Powering OpenAI’s GPT‑OSS Models on Modest Hardware
Link - https://medium.com/@rakshitaralimatti2001/4-bit-alchemy-how-mxfp4-makes-massive-models-like-gpt-oss-feasible-for-everyone-573d6630b56c
HF - https://huggingface.co/blog/RakshitAralimatti/learn-ai-with-me
- 1 reply
reacted to
ginipick's
post with 🔥
18 days ago
Post
2440
🚀 FLUXllama gpt-oss: 4-bit Quantization + GPT-OSS-120B = Perfect AI Image Generation
🎯 One-Line Summary
"Maximum Images with Minimal Memory!" - The perfect fusion of 4-bit quantization and GPT-OSS-120B prompt enhancement
ginipick/FLUXllama
🧠 Core Innovation: Prompt Enhancement System
📝 What You Type:
"cat"
✨ What GPT-OSS-120B Transforms:
"Majestic tabby cat with emerald eyes in golden afternoon light, soft bokeh, cinematic lighting, 8K photorealistic"
💡 Result: Beginners create professional-grade images instantly!
⚡ The Magic of 4-bit Quantization
🔥 Before (Standard Model)
📦 Memory: 24GB VRAM required
⏱️ Loading: 45 seconds
💰 Cost: RTX 4090 essential ($2000+)
🎉 After (FLUXllama gpt-oss 4-bit)
📦 Memory: 6GB VRAM (75% reduction!)
⏱️ Loading: 12 seconds (73% faster!)
💰 Cost: RTX 3060 works great! ($400)
Same quality, 4x efficiency! 🎊
🔧 Simple Model Swapping
python# Switch to any LLM in 1 second!
pipe = pipeline("text-generation", model="your-model")
✅ GPT-OSS-120B (Premium quality)
✅ Phi-3 (Lightning fast)
✅ Custom models (Your unique style)
🏆 Why FLUXllama gpt-oss?
💪 Powerful
Hugging Face 'STAR AI 12' Selected (Dec 2024)
95% quality maintained with 75% memory savings
🤝 Easy
No prompt writing skills needed
GPT-OSS-120B enhances automatically
💸 Economical
Works on consumer GPUs
60% cloud cost reduction
🚀 Start Now
Just 3 Steps!
💭 Enter your idea
✨ Click "Enhance Prompt"
🎨 Click "Generate"
Result: Images that rival pro designers!
🎊 FLUXllama gpt-oss = Less Resources + Smart Prompts = Best Images
Experience the perfect synergy of 4-bit quantization and GPT-OSS-120B!
🎯 One-Line Summary
"Maximum Images with Minimal Memory!" - The perfect fusion of 4-bit quantization and GPT-OSS-120B prompt enhancement
ginipick/FLUXllama
🧠 Core Innovation: Prompt Enhancement System
📝 What You Type:
"cat"
✨ What GPT-OSS-120B Transforms:
"Majestic tabby cat with emerald eyes in golden afternoon light, soft bokeh, cinematic lighting, 8K photorealistic"
💡 Result: Beginners create professional-grade images instantly!
⚡ The Magic of 4-bit Quantization
🔥 Before (Standard Model)
📦 Memory: 24GB VRAM required
⏱️ Loading: 45 seconds
💰 Cost: RTX 4090 essential ($2000+)
🎉 After (FLUXllama gpt-oss 4-bit)
📦 Memory: 6GB VRAM (75% reduction!)
⏱️ Loading: 12 seconds (73% faster!)
💰 Cost: RTX 3060 works great! ($400)
Same quality, 4x efficiency! 🎊
🔧 Simple Model Swapping
python# Switch to any LLM in 1 second!
pipe = pipeline("text-generation", model="your-model")
✅ GPT-OSS-120B (Premium quality)
✅ Phi-3 (Lightning fast)
✅ Custom models (Your unique style)
🏆 Why FLUXllama gpt-oss?
💪 Powerful
Hugging Face 'STAR AI 12' Selected (Dec 2024)
95% quality maintained with 75% memory savings
🤝 Easy
No prompt writing skills needed
GPT-OSS-120B enhances automatically
💸 Economical
Works on consumer GPUs
60% cloud cost reduction
🚀 Start Now
Just 3 Steps!
💭 Enter your idea
✨ Click "Enhance Prompt"
🎨 Click "Generate"
Result: Images that rival pro designers!
🎊 FLUXllama gpt-oss = Less Resources + Smart Prompts = Best Images
Experience the perfect synergy of 4-bit quantization and GPT-OSS-120B!
- 2 replies
reacted to
their
post with 👍
26 days ago
Post
280
🚀 Introducing Multi-Model RAG with LangChain!
Understand and query across images, tables, text, and files — all in one pipeline.
Get smart answers with relevant visuals or tables as references.
🔗 GitHub: https://github.com/rakshit2020/Multi-Model-RAG-LangChain
🎥 Demo video included — see it in action!
✅ Built for developers & researchers
⭐ Try it out, explore the code, and drop a star if you find it useful!
Understand and query across images, tables, text, and files — all in one pipeline.
Get smart answers with relevant visuals or tables as references.
🔗 GitHub: https://github.com/rakshit2020/Multi-Model-RAG-LangChain
🎥 Demo video included — see it in action!
✅ Built for developers & researchers
⭐ Try it out, explore the code, and drop a star if you find it useful!
posted
an
update
about 1 month ago
Post
280
🚀 Introducing Multi-Model RAG with LangChain!
Understand and query across images, tables, text, and files — all in one pipeline.
Get smart answers with relevant visuals or tables as references.
🔗 GitHub: https://github.com/rakshit2020/Multi-Model-RAG-LangChain
🎥 Demo video included — see it in action!
✅ Built for developers & researchers
⭐ Try it out, explore the code, and drop a star if you find it useful!
Understand and query across images, tables, text, and files — all in one pipeline.
Get smart answers with relevant visuals or tables as references.
🔗 GitHub: https://github.com/rakshit2020/Multi-Model-RAG-LangChain
🎥 Demo video included — see it in action!
✅ Built for developers & researchers
⭐ Try it out, explore the code, and drop a star if you find it useful!
upvoted
an
article
3 months ago
Article
Tiny Agents in Python: a MCP-powered agent in ~70 lines of code
By
and 3 others
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•
158
upvoted
a
paper
3 months ago
Qwen/Qwen3-8B-FP8
#1324 opened 4 months ago
by
RakshitAralimatti
upvoted
a
paper
6 months ago
reacted to
hexgrad's
post with 🔥
7 months ago
Post
7285
Wanted: Peak Data. I'm collecting audio data to train another TTS model:
+ AVM data: ChatGPT Advanced Voice Mode audio & text from source
+ Professional audio: Permissive (CC0, Apache, MIT, CC-BY)
This audio should *impress* most native speakers, not just barely pass their audio Turing tests. Professional-caliber means S or A-tier, not your average bloke off the street. Traditional TTS may not make the cut. Absolutely no low-fi microphone recordings like Common Voice.
The bar is much higher than last time, so there are no timelines yet and I expect it may take longer to collect such mythical data. Raising the bar means evicting quite a bit of old data, and voice/language availability may decrease. The theme is *quality* over quantity. I would rather have 1 hour of A/S-tier than 100 hours of mid data.
I have nothing to offer but the north star of a future Apache 2.0 TTS model, so prefer data that you *already have* and costs you *nothing extra* to send. Additionally, *all* the new data may be used to construct public, Apache 2.0 voicepacks, and if that arrangement doesn't work for you, no need to send any audio.
Last time I asked for horses; now I'm asking for unicorns. As of writing this post, I've currently got a few English & Chinese unicorns, but there is plenty of room in the stable. Find me over on Discord at
+ AVM data: ChatGPT Advanced Voice Mode audio & text from source
+ Professional audio: Permissive (CC0, Apache, MIT, CC-BY)
This audio should *impress* most native speakers, not just barely pass their audio Turing tests. Professional-caliber means S or A-tier, not your average bloke off the street. Traditional TTS may not make the cut. Absolutely no low-fi microphone recordings like Common Voice.
The bar is much higher than last time, so there are no timelines yet and I expect it may take longer to collect such mythical data. Raising the bar means evicting quite a bit of old data, and voice/language availability may decrease. The theme is *quality* over quantity. I would rather have 1 hour of A/S-tier than 100 hours of mid data.
I have nothing to offer but the north star of a future Apache 2.0 TTS model, so prefer data that you *already have* and costs you *nothing extra* to send. Additionally, *all* the new data may be used to construct public, Apache 2.0 voicepacks, and if that arrangement doesn't work for you, no need to send any audio.
Last time I asked for horses; now I'm asking for unicorns. As of writing this post, I've currently got a few English & Chinese unicorns, but there is plenty of room in the stable. Find me over on Discord at
rzvzn: https://discord.gg/QuGxSWBfQy
- 4 replies
published
a
model
7 months ago
updated
a
model
7 months ago
updated
2
models
8 months ago