Final recipe locked: Qwen3-MoE, 3 experts top-1, vocab 262144 (Gemma 3 SP, per-digit input wrap), GQA 3:1, Muon for hidden 2D weights and AdamW for embed and router, WSD with sqrt cooldown, beta2 ramp from 0.95 to 0.97, z-loss 1e-4 with gradients this time (the last build had a no_grad bug that silently killed it), Qwen3 aux loss coefficient 0.001, expert-load monitor that warns on starvation. Three phases: 8K pretrain, then 32K continued pretrain, then 8K SFT.
Shane PRO
Crownelius
AI & ML interests
LLM, RL, DL, ML, AGI, Distillation, Workflow Automation, Creative Writing
Recent Activity
updated a model 1 day ago
CompactAI-O/Shard-1 published a model 1 day ago
CompactAI-O/Shard-1 repliedto their post 1 day ago
Day 3 - 05/02/2026
Scamp ships, hits the wall. New plan...
Scamp came back from training today... Didn't go so well, I'm still unsure...
Fast benchmark, temperature 0.7, top_p 0.9:
- "Capital of France is" produced "covered by the Crown" (grammatical, factually wrong)
- "23 + 19 = ?" produced "23. Answer: 23. Answer: 23..." (loops, math broken)
- "def fibonacci(n):" produced a list of letters
It speaks English. It can't reason. At 8K vocab and 50M params, it was never going to.
Next build: 412M MoE-3E. Three experts (math, language, code), top-1 routing, random init, let specialization emerge from gradient signal alone. Tried seeded Branch-Train-MiX first then dropped it. Adds compute for no clear win when the router will find its own attractors anyway.
Big lesson today came from limit testing on A100 80GB. Surprise, every planned phase ran out of memory even on 80GB. Root cause: at vocab 262144 (Gemma 3 standard), the output logits dominate during forward and backward. Fix: Liger Kernel's fused cross-entropy. It streams the loss computation instead of materialising the full B by T by vocab tensor. Without it the build would not run.
Scamp proved the pipeline runs end-to-end on real hardware. The 412M run starts tomorrow. If routing balances naturally and math finally crystallises, ships as Crowfeather-412M-3E with GGUF in F16, Q8, Q5, and Q4.
So... the training may have produced a poet if I had done it better. But I didn't, so instead... we get a malformed robot named Scamp... This is progress.
-Shane
P.S Join discord for discussion: https://discord.gg/8ZscHNmJYE and
I post my finished stuff here: https://huggingface.co/CompactAI-OOrganizations
replied to their post 1 day ago
posted an update 1 day ago
Post
1625
Day 3 - 05/02/2026
Scamp ships, hits the wall. New plan...
Scamp came back from training today... Didn't go so well, I'm still unsure...
Fast benchmark, temperature 0.7, top_p 0.9:
- "Capital of France is" produced "covered by the Crown" (grammatical, factually wrong)
- "23 + 19 = ?" produced "23. Answer: 23. Answer: 23..." (loops, math broken)
- "def fibonacci(n):" produced a list of letters
It speaks English. It can't reason. At 8K vocab and 50M params, it was never going to.
Next build: 412M MoE-3E. Three experts (math, language, code), top-1 routing, random init, let specialization emerge from gradient signal alone. Tried seeded Branch-Train-MiX first then dropped it. Adds compute for no clear win when the router will find its own attractors anyway.
Big lesson today came from limit testing on A100 80GB. Surprise, every planned phase ran out of memory even on 80GB. Root cause: at vocab 262144 (Gemma 3 standard), the output logits dominate during forward and backward. Fix: Liger Kernel's fused cross-entropy. It streams the loss computation instead of materialising the full B by T by vocab tensor. Without it the build would not run.
Scamp proved the pipeline runs end-to-end on real hardware. The 412M run starts tomorrow. If routing balances naturally and math finally crystallises, ships as Crowfeather-412M-3E with GGUF in F16, Q8, Q5, and Q4.
So... the training may have produced a poet if I had done it better. But I didn't, so instead... we get a malformed robot named Scamp... This is progress.
-Shane
P.S Join discord for discussion: https://discord.gg/8ZscHNmJYE and
I post my finished stuff here: CompactAI-O
Scamp ships, hits the wall. New plan...
Scamp came back from training today... Didn't go so well, I'm still unsure...
Fast benchmark, temperature 0.7, top_p 0.9:
- "Capital of France is" produced "covered by the Crown" (grammatical, factually wrong)
- "23 + 19 = ?" produced "23. Answer: 23. Answer: 23..." (loops, math broken)
- "def fibonacci(n):" produced a list of letters
It speaks English. It can't reason. At 8K vocab and 50M params, it was never going to.
Next build: 412M MoE-3E. Three experts (math, language, code), top-1 routing, random init, let specialization emerge from gradient signal alone. Tried seeded Branch-Train-MiX first then dropped it. Adds compute for no clear win when the router will find its own attractors anyway.
Big lesson today came from limit testing on A100 80GB. Surprise, every planned phase ran out of memory even on 80GB. Root cause: at vocab 262144 (Gemma 3 standard), the output logits dominate during forward and backward. Fix: Liger Kernel's fused cross-entropy. It streams the loss computation instead of materialising the full B by T by vocab tensor. Without it the build would not run.
Scamp proved the pipeline runs end-to-end on real hardware. The 412M run starts tomorrow. If routing balances naturally and math finally crystallises, ships as Crowfeather-412M-3E with GGUF in F16, Q8, Q5, and Q4.
So... the training may have produced a poet if I had done it better. But I didn't, so instead... we get a malformed robot named Scamp... This is progress.
-Shane
P.S Join discord for discussion: https://discord.gg/8ZscHNmJYE and
I post my finished stuff here:
- 2 replies
replied to their post 1 day ago
I might. Join https://discord.gg/vaEquJ6UJT if you need to contact me.
posted an update 2 days ago
Post
3503
[DAY TWO] PROJECT CROWFEATHER - 5/1/2026
Que sera, what will he be?
Step 47,500 of 100,000. Loss hovering around 2.76 on 6.2B tokens. Throughput steady at 87k per second on the A100. Not a GH200, but she gets it done.
Still haven't named him. Scamp has a rascally charm. Quentin sounds like he'd wear a bow tie and think hard before speaking. Taking votes.
Phase two is what's keeping me up. Datasets everywhere and I can't pick. I'm fusing Google and DeepSeek's ideas: Gemma 4's alternating sliding and global attention, DeepSeek V4's Muon optimizer and WSD scheduler, Gemma 2's logit soft cap, and PaLM's z-loss. Sounds like peanut butter on a hamburger, but the loss curve says it works.
Tribe_v2 has real potential but needs more scaffolding than a barn raising before I throw it in. One thing's certain though. This model's gonna be a thinker. Not a Wikipedia parrot. Something that chews before it answers.
Finally got a use for my less popular datasets too. Some Opus-4.5-Writing-Style for polish. A few rows of Human-Archtypes-25k to see what personality bubbles up. Could be a poet, could be a grump. Either beats a flimsy fine-tune.
The bank's after my credit card. Until then, full steam.
Next model gets graphs. I swear.
-Shane
Que sera, what will he be?
Step 47,500 of 100,000. Loss hovering around 2.76 on 6.2B tokens. Throughput steady at 87k per second on the A100. Not a GH200, but she gets it done.
Still haven't named him. Scamp has a rascally charm. Quentin sounds like he'd wear a bow tie and think hard before speaking. Taking votes.
Phase two is what's keeping me up. Datasets everywhere and I can't pick. I'm fusing Google and DeepSeek's ideas: Gemma 4's alternating sliding and global attention, DeepSeek V4's Muon optimizer and WSD scheduler, Gemma 2's logit soft cap, and PaLM's z-loss. Sounds like peanut butter on a hamburger, but the loss curve says it works.
Tribe_v2 has real potential but needs more scaffolding than a barn raising before I throw it in. One thing's certain though. This model's gonna be a thinker. Not a Wikipedia parrot. Something that chews before it answers.
Finally got a use for my less popular datasets too. Some Opus-4.5-Writing-Style for polish. A few rows of Human-Archtypes-25k to see what personality bubbles up. Could be a poet, could be a grump. Either beats a flimsy fine-tune.
The bank's after my credit card. Until then, full steam.
Next model gets graphs. I swear.
-Shane
- 3 replies
replied to their post 3 days ago
Yes sir.
posted an update 3 days ago
Post
3763
[DAY ONE] PROJECT CROWFEATHER 4/30/2026
...The day I forgot to attach wandb.ai
Just dropped Crowfeather-50m, the first checkpoint in a series, and yeah, no graphs.
Crowfeather/Crowfeather-50m
54.5M params. Pretrain only. 17,500 steps banked on FineWeb-edu before Thunder credits ran dry. About 2.3B tokens, no SFT yet.
Architecture: Gemma-4 alternating sliding/global attention (1024 window, last layer always global) plus DeepSeek-V4 Muon optimizer plus WSD scheduler plus Gemma-2 logit soft-cap plus PaLM z-loss. Recipe in the model card.
What it can do: writes grammatical English. Knows that France has Rhine-adjacent monasteries (it picked Rouen instead of Paris but the vocabulary is in there). Tells stories about Mr. Fabien.
What it can't do yet: facts, code, math. Base LM, no SFT, no instruction tuning.
The series:
Every additional training run becomes another model card here
Every model card gets a matching post on this profile
Continuation goes to Colab next, picking up from step 17500 out of 100k
Limited to one post a day on Hugging Face, so updates will trickle out at that pace. Follow [@Crownelius](@Crownelius ) and [@Crowfeather]( Crowfeather ) if you want to watch this thing learn in public. Next drop will either come with the finished pre-train or whatever step I land on before the bank takes my credit card away.
Graphs will be available on my NEXT model lol
-Shane
...The day I forgot to attach wandb.ai
Just dropped Crowfeather-50m, the first checkpoint in a series, and yeah, no graphs.
Crowfeather/Crowfeather-50m
54.5M params. Pretrain only. 17,500 steps banked on FineWeb-edu before Thunder credits ran dry. About 2.3B tokens, no SFT yet.
Architecture: Gemma-4 alternating sliding/global attention (1024 window, last layer always global) plus DeepSeek-V4 Muon optimizer plus WSD scheduler plus Gemma-2 logit soft-cap plus PaLM z-loss. Recipe in the model card.
What it can do: writes grammatical English. Knows that France has Rhine-adjacent monasteries (it picked Rouen instead of Paris but the vocabulary is in there). Tells stories about Mr. Fabien.
What it can't do yet: facts, code, math. Base LM, no SFT, no instruction tuning.
The series:
Every additional training run becomes another model card here
Every model card gets a matching post on this profile
Continuation goes to Colab next, picking up from step 17500 out of 100k
Limited to one post a day on Hugging Face, so updates will trickle out at that pace. Follow [@Crownelius](@Crownelius ) and [@Crowfeather](
Graphs will be available on my NEXT model lol
-Shane
- 3 replies
reacted to danielhanchen's post with 🔥 3 days ago
Thank you!
reacted to Tonic's post with 👍 4 days ago
Post
3923
🙋🏻♂️ 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
posted an update 4 days ago
Post
5841
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!
- 3 replies
Agreed
reacted to imnotkitty's post with 🚀 about 2 months ago
Post
2283
The most popular OpenClaw tool has been released!
🥇 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)
🥇 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
1346
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