Post
8606
Unsloth is now one of the top 10 most followed organizations on Hugging Face. 🤗🦥
Thanks so much for all the support!
Our HF page:
unsloth
Thanks so much for all the support!
Our HF page:
- 5 replies
Shane PRO
Crownelius
AI & ML interests
LLM, RL, DL, ML, AGI, Distillation, Workflow Automation, Creative Writing
Recent Activity
updated a model about 2 hours ago
Crowfeather/Crowfeather-50m published a model about 2 hours ago
Crowfeather/Crowfeather-50mOrganizations
reacted to danielhanchen's post with 🔥 about 3 hours ago
Thank you!
reacted to Tonic's post with 👍 about 3 hours ago
Post
3416
🙋🏻♂️ Hey there folks,
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
Check this one out :
NuTonic/sat-bbox-metadata-sft-v1
the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .
hope you like it ! 🚀
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
Check this one out :
NuTonic/sat-bbox-metadata-sft-v1
the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .
hope you like it ! 🚀
- 2 replies
reacted to their post with 🔥 about 24 hours ago
Post
3290
My Huggingface journey has been a trip!
I wanted to take the time to thank each and every one of you for using my dataset and getting it to go as far as it did. Believe it or not, some neanderthal was and maybe still is trending on huggingface.
Not only did my dataset reach number one, my fine-tuned qwen3.5 model did as well. Top 10. Honestly, ain't much left to do here.
Y'all have given me the desire, no... the craving for more. I am absolutely obsessed with AI now. I want to tweak it... I want to take it apart, just to see what makes everything tick. I want to put it together like Frankenstein and his monster.
The only thing that's stopping this guy is compute. I don't mind spending every penny I have on this. I desperately want to drive AI forward, even just a little bit.
I never knew the clanker hater from a year ago would be saying this.
Thank you all from the bottom of my heart.
Looking forward to showing you what I'm cooking up next. @CompactAI is your only hint!
I wanted to take the time to thank each and every one of you for using my dataset and getting it to go as far as it did. Believe it or not, some neanderthal was and maybe still is trending on huggingface.
Not only did my dataset reach number one, my fine-tuned qwen3.5 model did as well. Top 10. Honestly, ain't much left to do here.
Y'all have given me the desire, no... the craving for more. I am absolutely obsessed with AI now. I want to tweak it... I want to take it apart, just to see what makes everything tick. I want to put it together like Frankenstein and his monster.
The only thing that's stopping this guy is compute. I don't mind spending every penny I have on this. I desperately want to drive AI forward, even just a little bit.
I never knew the clanker hater from a year ago would be saying this.
Thank you all from the bottom of my heart.
Looking forward to showing you what I'm cooking up next. @CompactAI is your only hint!
- 2 replies
posted an update about 24 hours ago
Post
3290
My Huggingface journey has been a trip!
I wanted to take the time to thank each and every one of you for using my dataset and getting it to go as far as it did. Believe it or not, some neanderthal was and maybe still is trending on huggingface.
Not only did my dataset reach number one, my fine-tuned qwen3.5 model did as well. Top 10. Honestly, ain't much left to do here.
Y'all have given me the desire, no... the craving for more. I am absolutely obsessed with AI now. I want to tweak it... I want to take it apart, just to see what makes everything tick. I want to put it together like Frankenstein and his monster.
The only thing that's stopping this guy is compute. I don't mind spending every penny I have on this. I desperately want to drive AI forward, even just a little bit.
I never knew the clanker hater from a year ago would be saying this.
Thank you all from the bottom of my heart.
Looking forward to showing you what I'm cooking up next. @CompactAI is your only hint!
I wanted to take the time to thank each and every one of you for using my dataset and getting it to go as far as it did. Believe it or not, some neanderthal was and maybe still is trending on huggingface.
Not only did my dataset reach number one, my fine-tuned qwen3.5 model did as well. Top 10. Honestly, ain't much left to do here.
Y'all have given me the desire, no... the craving for more. I am absolutely obsessed with AI now. I want to tweak it... I want to take it apart, just to see what makes everything tick. I want to put it together like Frankenstein and his monster.
The only thing that's stopping this guy is compute. I don't mind spending every penny I have on this. I desperately want to drive AI forward, even just a little bit.
I never knew the clanker hater from a year ago would be saying this.
Thank you all from the bottom of my heart.
Looking forward to showing you what I'm cooking up next. @CompactAI is your only hint!
- 2 replies
Agreed
reacted to imnotkitty's post with 🚀 about 2 months ago
Post
2279
The most popular OpenClaw tool has been released!
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🥈 OpenClaw Launch: Speed is king. Deploy your apps in under 30 seconds with a single click.
🥉 ClawTeam: Skip the setup. Get pre-configured AI agent blueprints built specifically for OpenClaw.
4️⃣ vibeclaw: Local-first. Run OpenClaw in your browser sandbox in literally 1 second.
5️⃣ Tinkerclaw: The startup favorite. Zero-code platform to deploy, manage, and scale AI assistants.
6️⃣ ClawWrapper: The "last mile" tool. Simplifies the entire packaging and launch process.
Which one are you adding to your stack? 🛠️
(Source: OpenClaw Directory)
🥇 Claw for All: The ultimate all-rounder. Simplifies deployment for both devs & pros with a seamless web/mobile experience.
🥈 OpenClaw Launch: Speed is king. Deploy your apps in under 30 seconds with a single click.
🥉 ClawTeam: Skip the setup. Get pre-configured AI agent blueprints built specifically for OpenClaw.
4️⃣ vibeclaw: Local-first. Run OpenClaw in your browser sandbox in literally 1 second.
5️⃣ Tinkerclaw: The startup favorite. Zero-code platform to deploy, manage, and scale AI assistants.
6️⃣ ClawWrapper: The "last mile" tool. Simplifies the entire packaging and launch process.
Which one are you adding to your stack? 🛠️
(Source: OpenClaw Directory)
- 2 replies
reacted to AbstractPhil's post with 👀 2 months ago
Post
1344
The Rosetta Stone geometric vocabulary and the ramping up capacity.
What makes this particular invariant special, is the existence within all structures I've tested so far. I had Claude write up the direct article based on what we built together, but I've tested it on many substructures. This is flawed, and I have a series of answers to making it more accurate.
First a reconstruction from the ground up. This means each shape is specifically built upward from the substructure to the point of inductive deviance. This will be less quick at first and then build speed as I optimize like the last system did.
The "saddle" problem; the system detected saddles because there wasn't enough deviance in the shapes to attenuate to more cardinality and more aligned substructures. The blobs were around 30-40% of the overall patches, which interpolated into the others produced a fair approximation.
It MOST DEFINITELY did see those shapes in their voxel complexity. This is real.
https://claude.ai/public/artifacts/bf1256c7-726d-4943-88ad-d6addb263b8b
You can play with a public claude artifact dedicated to viewing the current shape spectrum - and with that know exactly why it's flawed.
The flawed and repetitive shapes. I rapid prototyped and there are multiple redundant shapes that simply don't classify well or at all. Not to mention the rotation simply doesn't help much of the time, or doesn't exist with many shapes. This will be rectified in the next variation.
Projecting to shared latent space as a catalyst to allow growing subjective geoflow matched step variance, rather than simply direct classification. This will theoretically allow for full channel-to-channel invariant features to be mapped from structure to structure, and the very formula that encapsulated them to be directly baked into the math rather than classified as a substructure analysis.
There are many challenges between here and there, so stay tuned my friends as I plot the geometric language of pretrained AI.
What makes this particular invariant special, is the existence within all structures I've tested so far. I had Claude write up the direct article based on what we built together, but I've tested it on many substructures. This is flawed, and I have a series of answers to making it more accurate.
First a reconstruction from the ground up. This means each shape is specifically built upward from the substructure to the point of inductive deviance. This will be less quick at first and then build speed as I optimize like the last system did.
The "saddle" problem; the system detected saddles because there wasn't enough deviance in the shapes to attenuate to more cardinality and more aligned substructures. The blobs were around 30-40% of the overall patches, which interpolated into the others produced a fair approximation.
It MOST DEFINITELY did see those shapes in their voxel complexity. This is real.
https://claude.ai/public/artifacts/bf1256c7-726d-4943-88ad-d6addb263b8b
You can play with a public claude artifact dedicated to viewing the current shape spectrum - and with that know exactly why it's flawed.
The flawed and repetitive shapes. I rapid prototyped and there are multiple redundant shapes that simply don't classify well or at all. Not to mention the rotation simply doesn't help much of the time, or doesn't exist with many shapes. This will be rectified in the next variation.
Projecting to shared latent space as a catalyst to allow growing subjective geoflow matched step variance, rather than simply direct classification. This will theoretically allow for full channel-to-channel invariant features to be mapped from structure to structure, and the very formula that encapsulated them to be directly baked into the math rather than classified as a substructure analysis.
There are many challenges between here and there, so stay tuned my friends as I plot the geometric language of pretrained AI.
- 2 replies