@Datdanboi25 Factbase DCLM!
Mentioned!!!
💬 GPT-X3 when????
Dan P
Datdanboi25
AI & ML interests
Axiomic Labs Founder
Mechatronics engineer, LLMs, Vision Models, Embedding Models
Recent Activity
repliedto Banaxi-Tech's post about 13 hours ago
We ARE Announcing BananaMind 3 Ultra.
400M parameters, designed to beat LFM 2.5 1.2B.
This model is a usable small agent. Not a demo model like our 3M models.
FP8. KV1. Optimization. FactBase DCLM. Fable 5.2 for maximum speed kernels. TST.
This model will M.
What do you think? 50+ INT Index on the Open SLM leaderboard? BananaMind 3 is coming before christmas.
liked a dataset about 16 hours ago
slmconsortium/OpenThinkOrganizations
replied to Banaxi-Tech's post about 13 hours ago
reacted to Banaxi-Tech's post with ❤️🔥 5 days ago
Post
4,895
We're excited to release BananaMind 2.5 Pro.
It's done some post-training on our BananaMind 2 Pro base, via distillation of SmolLM2 360M.
It scores 28.98 INT INDEX compared to 24.96 of BananaMind 2 Pro on the AxiomicLabs/Open_SLM_Leaderboard
Check it out at BananaMind/BananaMind-2.5-Pro
BananaMind
@ArushCodes
@Bc-AI
It's done some post-training on our BananaMind 2 Pro base, via distillation of SmolLM2 360M.
It scores 28.98 INT INDEX compared to 24.96 of BananaMind 2 Pro on the AxiomicLabs/Open_SLM_Leaderboard
Check it out at BananaMind/BananaMind-2.5-Pro
@ArushCodes
@Bc-AI
- 53 replies
reacted to appvoid's post with ❤️ 9 days ago
Post
7,601
New leaderboard coming to the small language model community!
The leaderboard measures cognitive skills, which is more challenging and beyond language modeling and instruction-following. This is a first step towards more everyday useful and general small models.
dotlabs/cognirank
The leaderboard measures cognitive skills, which is more challenging and beyond language modeling and instruction-following. This is a first step towards more everyday useful and general small models.
dotlabs/cognirank
- 2 replies
reacted to Hoglet-33's post with ❤️ 17 days ago
Post
3,957
I wish Hugging Face posts had polls...
- 6 replies
replied to Banaxi-Tech's post 18 days ago
Head up bro, Excited for BM3!
reacted to appvoid's post with 🔥 20 days ago
Post
4,013
You know, I can't believe we are actually doing it, literally advancing the frontier at the edge! We've come a long way since the first models on open slm leaderboard, and, as many things that change, performance is one of the main things that has exploded.
7 years ago, the best we had was GPT-2. Today, we got 90m models beating smollm2! I can't barely see what a future with increasingly powerful small models will look like.
All I can say is that the best is yet to come. And will be agentic.
7 years ago, the best we had was GPT-2. Today, we got 90m models beating smollm2! I can't barely see what a future with increasingly powerful small models will look like.
All I can say is that the best is yet to come. And will be agentic.
- 11 replies
reacted to DedeProGames's post with 🔥 30 days ago
Post
3,468
Meet Supra2.5-Ultra-Preview-001. 230M parameters, ~40.4B training tokens.
SupraLabs has released the first preview of its Supra2.5-Ultra family: a compact, Llama-style base model for text completion and small-model experimentation.
What's inside?
• 230M parameters
• 32 transformer layers
• 2,048-token context window
• Grouped Query Attention: 12 attention heads, 4 KV heads
• 49,152-token vocabulary
• SwiGLU activation, RoPE and tied embeddings
• BF16 weights and Hugging Face Transformers support
• Apache 2.0 license
Training
The model was trained on approximately 40.4B tokens from FineWeb-Edu, DCLM-Baseline and FineMath using a single A100 SXM.
Reported zero-shot results
• HellaSwag: 37.18% normalized accuracy
• PIQA: 65.78% accuracy
• ARC-Easy: 57.11% accuracy
• ARC-Challenge: 26.28% normalized accuracy
Want to experiment?
Code on REPO.
Model, weights and usage example:
SupraLabs/Supra2.5-Ultra-Preview-001
SupraLabs has released the first preview of its Supra2.5-Ultra family: a compact, Llama-style base model for text completion and small-model experimentation.
What's inside?
• 230M parameters
• 32 transformer layers
• 2,048-token context window
• Grouped Query Attention: 12 attention heads, 4 KV heads
• 49,152-token vocabulary
• SwiGLU activation, RoPE and tied embeddings
• BF16 weights and Hugging Face Transformers support
• Apache 2.0 license
Training
The model was trained on approximately 40.4B tokens from FineWeb-Edu, DCLM-Baseline and FineMath using a single A100 SXM.
Reported zero-shot results
• HellaSwag: 37.18% normalized accuracy
• PIQA: 65.78% accuracy
• ARC-Easy: 57.11% accuracy
• ARC-Challenge: 26.28% normalized accuracy
Want to experiment?
Code on REPO.
Model, weights and usage example:
SupraLabs/Supra2.5-Ultra-Preview-001
- 4 replies
replied to Banaxi-Tech's post about 1 month ago
Head up bro, hyped for BM3!!!
reacted to Banaxi-Tech's post with 👍 about 1 month ago
Post
4,237
Should I even continue making models?
- 27 replies
Haha I wanna see them sooner
1:Individually Definitely!
3:a 150m!!!
replied to Banaxi-Tech's post about 1 month ago
Bro... I am sorry to break it down to you, but this is mathematically impossible, bcs your context itself eats up all the parameters. you end up with negative parameters (which is absolutely NOT a thing)😭
Your model is your embedding space.Anyways... good luck with whatever you are doing, man. research is research at the end of the day.
What is bro talm bout
replied to Banaxi-Tech's post about 1 month ago
Hyped for BananaMind3!!!
reacted to Banaxi-Tech's post with 🔥 about 1 month ago
Post
4,702
Well GPT X3 takes the lead. As of right now.
We are now announcing BananaMind 3 🍌! (not ai for those emoji guys)
All models will use BGA (which is almost just NSA) and our BM3X architecture.
Its sizes will be:
BananaMind 3 Flash Lite, 3M parameters at a context of 8K context.
BananaMind 3 Lite, 10M parameters with 16K context.
BananaMind 3 Flash, 25M Parameters with 16K context.
BananaMind 3 Pro, 50M parameters with 24K context.
BananaMind 3 Ultra, 100M parameters with 32K context.
And lastly, BananaMind 3 Max with 150M parameters and 64K CONTEXT.
I can assure you BananaMind 3 Max WILL beat GPT X3 or match it, we won't release it otherwise. We hope for a 40+ INTELLIGENCE INDEX!
BananaMind 3 may also be partnered with dot labs.
We will cancel BananaMind 2.1 and BananaMind 2 Ultra.
As of the BETU SLM Leaderboard we may need to release it after October 11, im very busy right now (even though we said We will release BETU leaderboard before Oct 11 😟)
We are now announcing BananaMind 3 🍌! (not ai for those emoji guys)
All models will use BGA (which is almost just NSA) and our BM3X architecture.
Its sizes will be:
BananaMind 3 Flash Lite, 3M parameters at a context of 8K context.
BananaMind 3 Lite, 10M parameters with 16K context.
BananaMind 3 Flash, 25M Parameters with 16K context.
BananaMind 3 Pro, 50M parameters with 24K context.
BananaMind 3 Ultra, 100M parameters with 32K context.
And lastly, BananaMind 3 Max with 150M parameters and 64K CONTEXT.
I can assure you BananaMind 3 Max WILL beat GPT X3 or match it, we won't release it otherwise. We hope for a 40+ INTELLIGENCE INDEX!
BananaMind 3 may also be partnered with dot labs.
We will cancel BananaMind 2.1 and BananaMind 2 Ultra.
As of the BETU SLM Leaderboard we may need to release it after October 11, im very busy right now (even though we said We will release BETU leaderboard before Oct 11 😟)
- 12 replies
reacted to raincandy-u's post with 🔥 about 1 month ago
Post
3,874
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 Hoglet-33's post with 🔥 about 2 months ago
Post
6,257
Hey everyone! I got sidetracked from my main projects and decided to test out the BananaAll app and see if I could make a small model not regress too much during SFT. Here is what happened:
The base model I chose was BananaMind/BananaMind-2.1-Pico-Preview, and the dataset I used was SupraLabs/SupraThink-Dataset-500x
I trained for 5 whole steps using a LoRA adapter.
Results:
A model that scores better on some benchmarks and worse on others, and still lacks most general capabilities.
You can find the model here: Hoglet-33/Hogleto
Credits:
- Thank you to @Banaxi-Tech for the BananaAll app (works perfectly on Windows and CPU)
- GPT-6 Sol for knowing how to merge some confusing files created by the app
- Myself for the idea
- Someone else somewhere who might have contributed to some of my ideas and might in the future
- And readers like you!
The base model I chose was BananaMind/BananaMind-2.1-Pico-Preview, and the dataset I used was SupraLabs/SupraThink-Dataset-500x
I trained for 5 whole steps using a LoRA adapter.
Results:
A model that scores better on some benchmarks and worse on others, and still lacks most general capabilities.
You can find the model here: Hoglet-33/Hogleto
Credits:
- Thank you to @Banaxi-Tech for the BananaAll app (works perfectly on Windows and CPU)
- GPT-6 Sol for knowing how to merge some confusing files created by the app
- Myself for the idea
- Someone else somewhere who might have contributed to some of my ideas and might in the future
- And readers like you!
- 6 replies
reacted to danielhanchen's post with 🔥🔥 about 2 months ago
reacted to their post with 🔥 about 2 months ago
Post
3,123
ForgePlex-M1-6M first model trained on AxiomicLabs TrainWork
ForgeWorks/ForgePlex-M1-6M just dropped from
ForgeWorks , and is the first model to ever be trained on our TrainWork training framework.
Achieving an Intelligence Index of 6.87 and taking #22 in the <10m category on the AxiomicLabs/Open_SLM_Leaderboard, very impressive work for a first model.
Give it some love!
ForgeWorks/ForgePlex-M1-6M just dropped from
Achieving an Intelligence Index of 6.87 and taking #22 in the <10m category on the AxiomicLabs/Open_SLM_Leaderboard, very impressive work for a first model.
Give it some love!
- 7 replies