Can't wait to see what the community ๐ชdo with this! ๐๐๐
๐ค pico mode
appvoid
appvoid
AI & ML interests
Working on small sota models
Recent Activity
new activity about 15 hours ago
appvoid/palmer-006:Author's Message new activity about 17 hours ago
moonshotai/Kimi-K3:They released the beast new activity about 18 hours ago
AxiomicLabs/Open_SLM_Leaderboard:New model palmer-006Organizations
replied to their post about 19 hours ago
posted an update about 20 hours ago
Post
101
A Small Model is All You Need. Meet
After 3 years of experiments, we are finally releasing our flagship tiny model: **palmer-006**.
If you are building for edge hardware, SBCs (Raspberry Pi, etc.), or low-power devices, this is for you. Inspired by Andrej Karpathy's idea of a self-contained "cognitive core," we wanted to see how much power we could pack into a sub-100M parameter footprint.
๐ **The cool part:**
As of today,
๐ง **How we "Palmerized" it:**
We believe in starting our experiments with the absolute strongest baseline possible.
1. Light fine-tuning on highly curated data
2. Model merging
3. Another light fine-tuning round
4. Adjusted Mamba for maximum token speed โก๏ธ
โ ๏ธ *Note: This is a foundational language model. It has not been instruction-tuned yet!*
Also, since this needs instruction tuning next to become a chat assistantโ**what dataset would you recommend we use for the instruct tune?**
---
๐ **Quick Links & Info:**
* **License:** Open for research, education, hobby, and modification! (For commercial use/hosted APIs, shoot an email to [email protected]. *PS: Donators can claim a free commercial license!*)
* **Attribution:** Built using AI tech from the Technology Innovation Institute (TII).
Can't wait to see what you build at the edge. Let me know your prompt completions below! ๐
appvoid/palmer-006
palmer-006 (90M)After 3 years of experiments, we are finally releasing our flagship tiny model: **palmer-006**.
If you are building for edge hardware, SBCs (Raspberry Pi, etc.), or low-power devices, this is for you. Inspired by Andrej Karpathy's idea of a self-contained "cognitive core," we wanted to see how much power we could pack into a sub-100M parameter footprint.
๐ **The cool part:**
As of today,
palmer-006 takes the **๐ฅ 2nd spot on ArithMark-3**, sitting only behind MobileLLM-R1-140M-base... while being **36% smaller**. It's a 90M parameter model hitting as a highly competitive baseline against 125M+ models.๐ง **How we "Palmerized" it:**
We believe in starting our experiments with the absolute strongest baseline possible.
1. Light fine-tuning on highly curated data
2. Model merging
3. Another light fine-tuning round
4. Adjusted Mamba for maximum token speed โก๏ธ
โ ๏ธ *Note: This is a foundational language model. It has not been instruction-tuned yet!*
Also, since this needs instruction tuning next to become a chat assistantโ**what dataset would you recommend we use for the instruct tune?**
---
๐ **Quick Links & Info:**
* **License:** Open for research, education, hobby, and modification! (For commercial use/hosted APIs, shoot an email to [email protected]. *PS: Donators can claim a free commercial license!*)
* **Attribution:** Built using AI tech from the Technology Innovation Institute (TII).
Can't wait to see what you build at the edge. Let me know your prompt completions below! ๐
appvoid/palmer-006
- 1 reply
reacted to AxionLab-official's post with โค๏ธโค๏ธ about 21 hours ago
Post
5109
โ ๏ธ Community Notice
We would like to clarify that SupraLabs has no affiliation, partnership, or connection whatsoever with "SupraLarps" or its members.
Please avoid interacting with their organization, repositories, or Spaces under the assumption that they are associated with us.
We are currently aware of the situation and have already contacted the appropriate channels to address it.
Thank you to everyone who continues to support SupraLabs. โค๏ธ
We would like to clarify that SupraLabs has no affiliation, partnership, or connection whatsoever with "SupraLarps" or its members.
Please avoid interacting with their organization, repositories, or Spaces under the assumption that they are associated with us.
We are currently aware of the situation and have already contacted the appropriate channels to address it.
Thank you to everyone who continues to support SupraLabs. โค๏ธ
- 12 replies
reacted to Banaxi-Tech's post with ๐ฅ about 22 hours ago
Post
4788
BananaMind 2 Pro is training!
The current checkpoint (ONLY 20% DONE) GETS #6 On the entire Open SLM Leaderboard.
We are going to release the first public preview on August 2-4 (estimated from speed)
Give us a follow to know when it releases!
The current checkpoint (ONLY 20% DONE) GETS #6 On the entire Open SLM Leaderboard.
We are going to release the first public preview on August 2-4 (estimated from speed)
Give us a follow to know when it releases!
- 2 replies
reacted to ucr-max's post with ๐ 2 days ago
Post
1006
Introducing Limen0.2B
We are releasing Limen0.2B, a 222.5M-parameter base language model developed as a research platform for efficient pretraining and superword tokenization at smaller scales.
Limen0.2B was trained from scratch on 50B tokens and uses a compact 16K BoundlessBPE vocabulary. The project explores whether SuperBPE-style tokenization can remain effective in a substantially smaller model and vocabulary regime than those examined in earlier large-scale experiments.
The model also combines a deep-and-narrow transformer design with Exclusive Self-Attention, grouped-query attention, and tied embeddings. Its compact vocabulary reduces the embedding footprint and leaves a larger share of the parameter budget available to the transformer layers.
Despite its relatively modest training budget, Limen0.2B achieves competitive results for its scale across the reported language understanding, commonsense reasoning, and grammatical evaluation tasks. Comparisons with other compact models are provided as context rather than strict rankings, as their training data, token budgets, architectures, and evaluation settings differ.
The release includes the model weights, implementation, training configuration, checkpoint progression, and evaluation results, all under Apache 2.0.
UniversalComputingResearch/Limen0.2B
Technical feedback, independent evaluations, and further experiments with the model and tokenizer are welcome.
We are releasing Limen0.2B, a 222.5M-parameter base language model developed as a research platform for efficient pretraining and superword tokenization at smaller scales.
Limen0.2B was trained from scratch on 50B tokens and uses a compact 16K BoundlessBPE vocabulary. The project explores whether SuperBPE-style tokenization can remain effective in a substantially smaller model and vocabulary regime than those examined in earlier large-scale experiments.
The model also combines a deep-and-narrow transformer design with Exclusive Self-Attention, grouped-query attention, and tied embeddings. Its compact vocabulary reduces the embedding footprint and leaves a larger share of the parameter budget available to the transformer layers.
Despite its relatively modest training budget, Limen0.2B achieves competitive results for its scale across the reported language understanding, commonsense reasoning, and grammatical evaluation tasks. Comparisons with other compact models are provided as context rather than strict rankings, as their training data, token budgets, architectures, and evaluation settings differ.
The release includes the model weights, implementation, training configuration, checkpoint progression, and evaluation results, all under Apache 2.0.
UniversalComputingResearch/Limen0.2B
Technical feedback, independent evaluations, and further experiments with the model and tokenizer are welcome.
replied to Banaxi-Tech's post 2 days ago
Cool stuff right there! Keep it up
reacted to Banaxi-Tech's post with ๐ฅ 2 days ago
Post
3499
We're excited to release BananaMindBench Leaderboard, our leaderboard for BananaMind Base Bench 1.1.
It measures model performance on a variety of different tasks:
Language Completion
Common sense too
World Knowledge
Context Tracking
Quantitative
Logical Reasoning
Code Completion
Each has a different score and 1 overall score.
Submit your own model:
BananaMind/BananaMindBench-Leaderboard
Check it out:
BananaMind/BananaMindBench-Leaderboard
It measures model performance on a variety of different tasks:
Language Completion
Common sense too
World Knowledge
Context Tracking
Quantitative
Logical Reasoning
Code Completion
Each has a different score and 1 overall score.
Submit your own model:
BananaMind/BananaMindBench-Leaderboard
Check it out:
BananaMind/BananaMindBench-Leaderboard
- 10 replies
reacted to Nymbo's post with ๐ฅ 3 days ago
Post
5120
Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.
See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2
Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.
See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2
Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
- 5 replies
replied to their post 3 days ago
because its my own and im currently training it
you should do more of that magic you did with hellaswag on BananaMind-2-Medium
replied to their post 5 days ago
BananaMind 2 pro... ok i know
Let's Go!!!
replied to their post 5 days ago
One is Kimi-K3, which I have heard of briefly. What's the other one? I hope it's a video generation model
You guessed right! One of them is the best biggest model. The other one is the best smallest.
posted an update 6 days ago
Post
3313
Two big projects are open sourced soon. Get ready...
- 11 replies
replied to their post 8 days ago
Following
replied to their post 8 days ago
Hey
following
replied to their post 8 days ago
mine are all <300M
Great, following already!
replied to their post 8 days ago
Surely I do!
posted an update 8 days ago
Post
211
If you make cool smol ๐ค๐ค models (below 0.5b parameters), leave a reply and I will follow you! I'm serious, you don't need to follow me at all just share something through the replies and I (and potentially more people) will follow you (if your models are decent ofc).
- 11 replies
replied to their post 9 days ago
following