GradyanAkincilari/1200-R1-0528-v2-mermaid-fixed
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alkinun
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LLMs, Alignment, Merging, Unsloth, DPO, SFT, ORPO, SPIN..
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unsloth/DeepSeek-R1-0528-Qwen3-8B-unsloth-bnb-4bit
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FlameF0X's
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SnowflakeCore-G1 development update: We're building a 24-layer transformer with 32K context and 1024 embedding dimensions - pretty ambitious! Even running at batch_size=1 with heavy gradient accumulation, we're hitting memory walls at 300GB RAM. Scaling up to ~1TB will take some time, but the architecture is looking promising. Thanks for following along with the journey! 😅
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samihalawa's
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🔥BEST DEBUG PROMPT FOR CLAUDE_CODE
😲FIXES ANY REPO
😲FIXES ANY REPO
How can I prompt you so there will not be any More bug or thing? So list the 20 kind of errors and bugs more common in a codebase like this one and create a comprehensive table list of each of them and all the main files and fucnionality and fix until cross all out. Don't add complexity. Ignore security. Only fatal problems
Proceed in the most exhaustive and comprehensive way possible. Don't miss a line of code. SYSTEMATICALLY FIX EVERYTHING
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jsulz's
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It's been a bit since I took a step back and looked at
xet-team
progress to migrate Hugging Face from Git LFS to Xet, but every time I do it boggles the mind.
A month ago there were 5,500 users/orgs on Xet with 150K repos and 4PB. Today?
🤗 700,000 users/orgs
📈 350,000 repos
🚀 15PB
Meanwhile, our migrations have pushed throughput to numbers that are bonkers. In June, we hit upload speeds of 577Gb/s (crossing 500Gb/s for the first time).
These are hard numbers to put into context, but let's try:
The latest run of the Common Crawl from
commoncrawl
was 471 TB.
We now have ~32 crawls stored in Xet. At peak upload speed we could move the latest crawl into Xet in about two hours.
We're moving to a new phase in the process, so stay tuned.
This shift in gears means it's also time to roll up our sleeves and look at all the bytes we have and the value we're adding to the community.
I already have some homework from @RichardErkhov to look at the dedupe across their uploads, and I'll be doing the same for other early adopters, big models/datasets, and frequent uploaders (looking at you @bartowski 👀)
Let me know if there's anything you're interested in; happy to dig in!
xet-team
progress to migrate Hugging Face from Git LFS to Xet, but every time I do it boggles the mind.A month ago there were 5,500 users/orgs on Xet with 150K repos and 4PB. Today?
🤗 700,000 users/orgs
📈 350,000 repos
🚀 15PB
Meanwhile, our migrations have pushed throughput to numbers that are bonkers. In June, we hit upload speeds of 577Gb/s (crossing 500Gb/s for the first time).
These are hard numbers to put into context, but let's try:
The latest run of the Common Crawl from
commoncrawl
was 471 TB. We now have ~32 crawls stored in Xet. At peak upload speed we could move the latest crawl into Xet in about two hours.
We're moving to a new phase in the process, so stay tuned.
This shift in gears means it's also time to roll up our sleeves and look at all the bytes we have and the value we're adding to the community.
I already have some homework from @RichardErkhov to look at the dedupe across their uploads, and I'll be doing the same for other early adopters, big models/datasets, and frequent uploaders (looking at you @bartowski 👀)
Let me know if there's anything you're interested in; happy to dig in!
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Great job fellas keep it up!
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yeonseok-zeticai's
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2966
🚀 Real-Time On-Device AI Agent with Polaris-4B — Run It Yourself, No Cloud, No Cost
We just deployed a real-time on-device AI agent using the Polaris-4B-Preview model — one of the top-performing <6B open LLMs on Hugging Face.
📱 What’s remarkable?
This model runs entirely on a mobile device, without cloud, and without any manual optimization. It was built using ZETIC.MLange, and the best part?
➡️ It’s totally automated, free to use, and anyone can do it.
You don’t need to write deployment code, tweak backends, or touch device-specific SDKs. Just upload your model — and ZETIC.MLange handles the rest.
🧠 About the Model
- Model: Polaris-4B-Preview
- Size: ~4B parameters
- Ranking: Top 3 on Hugging Face LLM Leaderboard (<6B)
- Tokenizer: Token-incremental inference supported
- Modifications: None — stock weights, just optimized for mobile
⚙️ What ZETIC.MLange Does
ZETIC.MLange is a fully automated deployment framework for On-Device AI, built for AI engineers who want to focus on models — not infrastructure.
Here’s what it does in minutes:
- 📊 Analyzes model structure
- ⚙️ Converts to mobile-optimized format (e.g., GGUF, ONNX)
- 📦 Generates a runnable runtime environment with pre/post-processing
- 📱 Targets real mobile hardware (CPU, GPU, NPU — including Qualcomm, MediaTek, Apple)
- 🎯 Gives you a downloadable SDK or mobile app component — ready to run
And yes — this is available now, for free, at https://mlange.zetic.ai
🧪 For AI Engineers Like You, If you want to:
- Test LLMs directly on-device
- Run models offline with no latency
- Avoid cloud GPU costs
- Deploy to mobile without writing app-side inference code
Then this is your moment. You can do exactly what we did, using your own models — all in a few clicks.
🎯 Start here → https://mlange.zetic.ai
📬 Want to try Polaris-4B on your own app? [email protected], or just visit https://mlange.zetic.ai , it is opened as free!
Great work @Chancy , @Zhihui , @tobiaslee !
We just deployed a real-time on-device AI agent using the Polaris-4B-Preview model — one of the top-performing <6B open LLMs on Hugging Face.
📱 What’s remarkable?
This model runs entirely on a mobile device, without cloud, and without any manual optimization. It was built using ZETIC.MLange, and the best part?
➡️ It’s totally automated, free to use, and anyone can do it.
You don’t need to write deployment code, tweak backends, or touch device-specific SDKs. Just upload your model — and ZETIC.MLange handles the rest.
🧠 About the Model
- Model: Polaris-4B-Preview
- Size: ~4B parameters
- Ranking: Top 3 on Hugging Face LLM Leaderboard (<6B)
- Tokenizer: Token-incremental inference supported
- Modifications: None — stock weights, just optimized for mobile
⚙️ What ZETIC.MLange Does
ZETIC.MLange is a fully automated deployment framework for On-Device AI, built for AI engineers who want to focus on models — not infrastructure.
Here’s what it does in minutes:
- 📊 Analyzes model structure
- ⚙️ Converts to mobile-optimized format (e.g., GGUF, ONNX)
- 📦 Generates a runnable runtime environment with pre/post-processing
- 📱 Targets real mobile hardware (CPU, GPU, NPU — including Qualcomm, MediaTek, Apple)
- 🎯 Gives you a downloadable SDK or mobile app component — ready to run
And yes — this is available now, for free, at https://mlange.zetic.ai
🧪 For AI Engineers Like You, If you want to:
- Test LLMs directly on-device
- Run models offline with no latency
- Avoid cloud GPU costs
- Deploy to mobile without writing app-side inference code
Then this is your moment. You can do exactly what we did, using your own models — all in a few clicks.
🎯 Start here → https://mlange.zetic.ai
📬 Want to try Polaris-4B on your own app? [email protected], or just visit https://mlange.zetic.ai , it is opened as free!
Great work @Chancy , @Zhihui , @tobiaslee !
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BFFree's
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6 days ago
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Love the subtle retro style of this sculptural piece! Took a bit of fine tuning the style but one of the more satisfying things I have created recently.
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1721
Trinity-Synthesis: A Multi-Agent Architecture for AI Agents That Think Before They Speak
Ever felt your AI agent is "shooting from the hip"? It latches onto a single line of thought and fails to produce a robust, well-rounded plan. This is a common struggle I've called the "AI Reasoning Paradox."
To tackle this, I developed Trinity-Synthesis, a multi-agent architecture designed to force reflection and synthesis before delivering a final answer. The philosophy is simple: constructive conflict between different perspectives leads to better solutions.
Here’s the core idea:
Instead of one agent, it uses four agents running on the same base model but with different "personalities" defined by their system prompts and temperature settings:
🧠 The Visionary: Thinks outside the box (high temp: 1.0).
📊 The Analyst: Focuses on logic, data, and structure (low temp: 0.3).
🛠️ The Pragmatist: Evaluates feasibility, costs, and risks (mid temp: 0.5).
These three "thinkers" work in parallel on the same problem. Then, a final Synthesizer agent critically analyzes their outputs, rejects flawed arguments, and integrates the best points into a single, coherent, and often superior strategy.
The result is a more robust reasoning process that balances creativity with analytical rigor, making it ideal for solving complex, strategic problems where answer quality is critical.
I've written a deep dive on how it works, including a detailed case study ("The Helios Initiative") and the Python source code for you to experiment with.
Read the full article on Medium:
https://medium.com/@brainhome9/trinity-synthesis-how-i-built-an-ai-agent-that-thinks-before-it-speaks-d45d45c2827c
I'd love to hear your feedback and see what you build with it!
#AI #AIAgents #LLM #Reasoning #MultiAgent
Ever felt your AI agent is "shooting from the hip"? It latches onto a single line of thought and fails to produce a robust, well-rounded plan. This is a common struggle I've called the "AI Reasoning Paradox."
To tackle this, I developed Trinity-Synthesis, a multi-agent architecture designed to force reflection and synthesis before delivering a final answer. The philosophy is simple: constructive conflict between different perspectives leads to better solutions.
Here’s the core idea:
Instead of one agent, it uses four agents running on the same base model but with different "personalities" defined by their system prompts and temperature settings:
🧠 The Visionary: Thinks outside the box (high temp: 1.0).
📊 The Analyst: Focuses on logic, data, and structure (low temp: 0.3).
🛠️ The Pragmatist: Evaluates feasibility, costs, and risks (mid temp: 0.5).
These three "thinkers" work in parallel on the same problem. Then, a final Synthesizer agent critically analyzes their outputs, rejects flawed arguments, and integrates the best points into a single, coherent, and often superior strategy.
The result is a more robust reasoning process that balances creativity with analytical rigor, making it ideal for solving complex, strategic problems where answer quality is critical.
I've written a deep dive on how it works, including a detailed case study ("The Helios Initiative") and the Python source code for you to experiment with.
Read the full article on Medium:
https://medium.com/@brainhome9/trinity-synthesis-how-i-built-an-ai-agent-that-thinks-before-it-speaks-d45d45c2827c
I'd love to hear your feedback and see what you build with it!
#AI #AIAgents #LLM #Reasoning #MultiAgent
- 4 replies