Supra2 100M, BananaMind 2 Pro and GPT-X2.5-135M all releasing at the same time is exciting!
We might going to make Supra2-1B, MAYBE
๐๏ธ Building on HF
AxionLab
AxionLab-official
AI & ML interests
Owner of SupraLabs and iGPU Lover
Recent Activity
new activity about 1 hour ago
AxiomicLabs/Open_SLM_Leaderboard:Can you add SupraElegans-500k please? liked a model about 9 hours ago
SupraLabs/SupraElegans-500k liked a model about 10 hours ago
SupraLabs/supra-title-50m-preOrganizations
reacted to Enderchef's post with ๐คโค๏ธ๐๐ฅ 5 days ago
Post
3399
๐ Supra2 100M is out, and multiple other SLM orgs are gaining power!
Following takes a press. Please follow:
fromziro
SupraLabs
AxiomicLabs
Following takes a press. Please follow:
- 8 replies
replied to LH-Tech-AI's post 5 days ago
reacted to LH-Tech-AI's post with ๐คฏ๐๐ฅ 5 days ago
Post
3398
Supra2-100M is out!
Go check it out:
- https://www.reddit.com/r/LocalLLaMA/comments/1velyl9/new_models_supra2100m_base_and_instruct_go_check/
- https://huggingface.co/SupraLabs/Supra2-100M
- SupraLabs/Supra2-100M-Instruct
Give us a like and a follow!!
HAVE FUN ๐ค๐ฅ๐
more coming soon...
Go check it out:
- https://www.reddit.com/r/LocalLLaMA/comments/1velyl9/new_models_supra2100m_base_and_instruct_go_check/
- https://huggingface.co/SupraLabs/Supra2-100M
- SupraLabs/Supra2-100M-Instruct
Give us a like and a follow!!
HAVE FUN ๐ค๐ฅ๐
more coming soon...
- 1 reply
reacted to their post with ๐ฅ 6 days ago
posted an update 6 days ago
reacted to LH-Tech-AI's post with โค๏ธ 7 days ago
Post
2381
Announcing The Supra2 Family And Supra2-100M
Today, we are announcing a brand-new series of SupraLabs models: Supra2
This series will feature various models, including such as:
- ๐ Supra2-Nano (0.4M) โ The smallest Supra2 model.
- ๐ค Supra2-Small (1.4M) โ The tiny model that runs everywhere.
- ๐ช Supra2-Medium (25M) โ Our medium class model in the Supra2 family. The powerful midsizer.
- ๐ฅ Supra2-Pro (100M): base, instruct, reasoning, code, math and more! โ The most capable model yet! A real allrounder for all your everyday tasks.
- ๐จ Supra2-IMG โ our generative text-to-image model
...and many more...
Current progress:
- Nano (0.4M) and Small (1.4M): in training; almost done. Baseline set.
- Medium (25M): coming soon...
- Pro (100M): in training; finishes in 66 hours - Monday, 3rd August 2026, 12:00AM
- IMG: coming soon...
You can support us with a like and follow if you want!
Don't miss our next release! Stay tuned...
Today, we are announcing a brand-new series of SupraLabs models: Supra2
This series will feature various models, including such as:
- ๐ Supra2-Nano (0.4M) โ The smallest Supra2 model.
- ๐ค Supra2-Small (1.4M) โ The tiny model that runs everywhere.
- ๐ช Supra2-Medium (25M) โ Our medium class model in the Supra2 family. The powerful midsizer.
- ๐ฅ Supra2-Pro (100M): base, instruct, reasoning, code, math and more! โ The most capable model yet! A real allrounder for all your everyday tasks.
- ๐จ Supra2-IMG โ our generative text-to-image model
...and many more...
Current progress:
- Nano (0.4M) and Small (1.4M): in training; almost done. Baseline set.
- Medium (25M): coming soon...
- Pro (100M): in training; finishes in 66 hours - Monday, 3rd August 2026, 12:00AM
- IMG: coming soon...
You can support us with a like and follow if you want!
Don't miss our next release! Stay tuned...
- 30 replies
reacted to their post with ๐ง ๐๐ฅ 7 days ago
Post
11063
THIS IS CRAZY! THE MODEL ON THE IMAGE(Supra-50M-Reasoning) answered correctly and its QUANTIZED IN 2BIT! THE RESPONSE IS CORRECT, IN A 15MB SIZE FILE!
- 15 replies
reacted to OppaAI's post with ๐ 7 days ago
Post
1076
After a month of interacting with my AI Waifu, I noticed a few issues in the system; so I decided to spend this week revisiting the systems implemented in Phase 1.0, 1.5 and 2.0, and try to make them to be more like production-grade as much as possible:
1) Memory Degradation - recalled memories are not as good as in the beginning, causing AI Waifu to be more chaotic as she hallucinates over contaminated memories like a bad vicious cycle.
So I transformed the original stateless sqlite-vec vector store to be a simple entity co-mention graph. And even make a studio to visualize the memories stored inside the vector db.
Just by looking at the graph, I saw a couple issues:
a) After 1.5 months of interactions, there should be only one month of pinned memory (in green) over 1.5 months of active memory (in purple). How come pinned memory is in majority over active ones?
I suppose the forgetting curve I had set too aggressive and memory half-life and shelf life too short, active memory got decayed way before monthly consolidation and got lost forever.
b) I saw she memorized me into 3 different entities: my username, my nickname and my Github user ID (leaked into pinned memory, presumbly during nightly dreaming process). 3B small param LLM has hard time to correlation 3 different entities into single person, I may have to harden into one.
2) RAM burst during voice input - for some reason the tensor calculation of SileroVAD of the voice input uses PyTorch, and that's the only place in the whole codebase using torch after removing it from TTS synthesization. By switching to SileroVAD-onnx integrated in the ASR sherpa-onnx, the RAM usage drops at least 0.5GB (after shaving off ~1GB from TTS) by completely remove PyTorch dependencies.
3) Introduced a better Wake Word system using Livekit-Wake word instead of using ASR to do the wake word activation to save computation. Optional features like Speak Verification, Barge-in sensitivity, etc, need to find the optimum settings.
1) Memory Degradation - recalled memories are not as good as in the beginning, causing AI Waifu to be more chaotic as she hallucinates over contaminated memories like a bad vicious cycle.
So I transformed the original stateless sqlite-vec vector store to be a simple entity co-mention graph. And even make a studio to visualize the memories stored inside the vector db.
Just by looking at the graph, I saw a couple issues:
a) After 1.5 months of interactions, there should be only one month of pinned memory (in green) over 1.5 months of active memory (in purple). How come pinned memory is in majority over active ones?
I suppose the forgetting curve I had set too aggressive and memory half-life and shelf life too short, active memory got decayed way before monthly consolidation and got lost forever.
b) I saw she memorized me into 3 different entities: my username, my nickname and my Github user ID (leaked into pinned memory, presumbly during nightly dreaming process). 3B small param LLM has hard time to correlation 3 different entities into single person, I may have to harden into one.
2) RAM burst during voice input - for some reason the tensor calculation of SileroVAD of the voice input uses PyTorch, and that's the only place in the whole codebase using torch after removing it from TTS synthesization. By switching to SileroVAD-onnx integrated in the ASR sherpa-onnx, the RAM usage drops at least 0.5GB (after shaving off ~1GB from TTS) by completely remove PyTorch dependencies.
3) Introduced a better Wake Word system using Livekit-Wake word instead of using ASR to do the wake word activation to save computation. Optional features like Speak Verification, Barge-in sensitivity, etc, need to find the optimum settings.
reacted to Enderchef's post with ๐ง ๐ค๐ฅโค๏ธ 8 days ago
Post
2364
I've left Glint Research.
After a long time in Glint Research, an entire distributed training grid built free for them, and more, I've decided that I no longer want to be affiliated with Glint Research.
More updates will follow. Comments/questions are welcome.
While you're reading this, follow these orgs! Following takes just a few seconds, and can change someone's day.
AxiomicLabs
fromziro
SupraLabs
After a long time in Glint Research, an entire distributed training grid built free for them, and more, I've decided that I no longer want to be affiliated with Glint Research.
More updates will follow. Comments/questions are welcome.
While you're reading this, follow these orgs! Following takes just a few seconds, and can change someone's day.
- 2 replies