AxiomicLabs/GPT-X3-150M
Text Generation • 0.1B • Updated • 338 • 40
🔄 In a Training Loop
Inflammable1230
Inflammable1230
P(doom) 1%
AI & ML interests
Edge AI enthusiast, 16GB CPU, Falcon H1 90M = favorite SLM.
Recent Activity
liked a model about 6 hours ago
AxiomicLabs/GPT-X3-150M liked a model about 6 hours ago
LiquidAI/d1-omni-600MOrganizations
None yet
liked 2
models about 6 hours ago
reacted to AtAndDev's post with 👍 about 6 hours ago
Post
2714
@Banaxi-Tech stop hiding my comments. AND STOP STEALING PAPERS AND SPREADING MISINFORMATION.
your BGA blog is a copy of NSA (deepseek, 2025) branded under your name. literally the same top16 selected blocks, 512 local window, router over block summaries, all you did was change block size from 64 to 128.
you didnt cite NSA once but you put a “please cite BGA” bibtex at the bottom.
i commented under your post and said that there is no way that you can support claims like: “The Accuracy Should BE WAy better than DSA but untested yet.” you didnt run a single experiment. and the 256x isnt from BGA, its just n/2k with k=2048 so the exact same k DSA uses. if opus wrote this for you, at least read it before posting.
i commented again after you hid my comment despite it having constructive and correct feedback and you hid that too. and again.
you can hide the truth and just try to get hf post likes..... but is it really the thing that needs to be done? do you really want to take papers and make them yours while barely even changing the params?
admitting your mistakes and doing something about them needs humbleness, intelligence, humanness.
i encourage you to admit your mistakes and try to do better next time (at least read what blog your ai wrote or do proper experiments to back your stuff up).
your BGA blog is a copy of NSA (deepseek, 2025) branded under your name. literally the same top16 selected blocks, 512 local window, router over block summaries, all you did was change block size from 64 to 128.
you didnt cite NSA once but you put a “please cite BGA” bibtex at the bottom.
i commented under your post and said that there is no way that you can support claims like: “The Accuracy Should BE WAy better than DSA but untested yet.” you didnt run a single experiment. and the 256x isnt from BGA, its just n/2k with k=2048 so the exact same k DSA uses. if opus wrote this for you, at least read it before posting.
i commented again after you hid my comment despite it having constructive and correct feedback and you hid that too. and again.
you can hide the truth and just try to get hf post likes..... but is it really the thing that needs to be done? do you really want to take papers and make them yours while barely even changing the params?
admitting your mistakes and doing something about them needs humbleness, intelligence, humanness.
i encourage you to admit your mistakes and try to do better next time (at least read what blog your ai wrote or do proper experiments to back your stuff up).
- 44 replies
liked a
model about 7 hours ago
liked 3
models 2 days ago
liked a
Space 3 days ago
upvoted a changelog 3 days ago
Hugging Face Changelog
P(doom) on user profiles
• 339
liked a
model 3 days ago
reacted to raincandy-u's post with 🔥 4 days ago
Post
3737
20K parameters can tell a story. 🚀
🤗 We trained a ~20k-parameter Transformer that can actually write stories!
raincandy-u/MacroStories
→ ~50× smaller than the 1M-parameter TinyStories model
→ ~3,000× smaller than AlexNet
→ 81 KB in FP32
yayyy the whole model. ૮ ˶ᵔ ᵕ ᵔ˶ ა
She has a 32-dimensional hidden state, a 378-token vocabulary, and just one decoder block — recurrently applied 4 times with shared weights.
Despite having only 19,969 parameters, she can maintain a simple narrative across 100–300 words: establish a goal, encounter a problem, take relevant actions, and reach an outcome.
She runs extremely fast on CPU — no GPU required. The entire model is tiny enough to load almost instantly! ☺️
🤗 We trained a ~20k-parameter Transformer that can actually write stories!
raincandy-u/MacroStories
→ ~50× smaller than the 1M-parameter TinyStories model
→ ~3,000× smaller than AlexNet
→ 81 KB in FP32
yayyy the whole model. ૮ ˶ᵔ ᵕ ᵔ˶ ა
She has a 32-dimensional hidden state, a 378-token vocabulary, and just one decoder block — recurrently applied 4 times with shared weights.
Despite having only 19,969 parameters, she can maintain a simple narrative across 100–300 words: establish a goal, encounter a problem, take relevant actions, and reach an outcome.
She runs extremely fast on CPU — no GPU required. The entire model is tiny enough to load almost instantly! ☺️
- 3 replies
reacted to DedeProGames's post with 🔥 4 days ago
Post
3999
Im working on a 23M ASR model, trained on 100k hours of audio
- 5 replies
liked a
Space 4 days ago
upvoted a paper 4 days ago
liked 2
models 4 days ago
liked a
Space 4 days ago
liked a
model 5 days ago
upvoted a collection 5 days ago
liked a
Space 5 days ago