Post
2860
ACR 1.0 launch is being prepared and researched now!
Also I'm going to vacation tomorrow but it should still be released!
saicr
Also I'm going to vacation tomorrow but it should still be released!
- 1 reply
saicr
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AI & ML interests
Safe Artificial Intelligence Consciousness Research
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Banaxi-Tech
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Banaxi-Tech
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7576
We're releasing a MAJOR update to the BananaAll SLM Super App.
If you want to use a custom architecture, previously you had to go trough reviewing the code yourself, now add an Openrouter API key and review it with GPT 6 Luna in one button. A review cost be half a cent so anyone can try it. This is one of the main features.
Now ROCm, AMD and Windows, Mac support.
Colab and Molab support.
Detailed list of features:
Get improved Windows Python detection and support paths for compatible AMD ROCm, Intel XPU, and Apple MPS setups.
Choose local training or export a self-contained Python script for Colab or Molab. Notebook runs produce a downloadable model ZIP.
Start pretraining with an existing model’s tokenizer, or train a new one from your datasets.
Try experimental 1.58-bit Ternary fake-quantized training on NVIDIA GPUs.
Watch live tokens per second. Model compilation is on by default and falls back automatically if it fails.
Build custom architectures with separate configuration and modeling files, then review the training code manually or with optional OpenRouter AI Review.
Install from source with the new coding-agent instructions.
This release also fixes inflated loss reporting for custom models.
And for those users who didn't want to try it out just because installation would be so hard, it isnt now.
Go to any coding agent (Pi, Claude Code, Codex, OpenCode, basically all work), and just paste "Install BananaAll for me. Fetch and follow https://raw.githubusercontent.com/BananaMind/BananaAll/main/agent_install.txt."
That's it.
Check it out at https://github.com/BananaMind/BananaAll/
Also on SAICR, we're currently training a new major model (NACR v2) and ACR 1.0 is in the finishing.
If you want to use a custom architecture, previously you had to go trough reviewing the code yourself, now add an Openrouter API key and review it with GPT 6 Luna in one button. A review cost be half a cent so anyone can try it. This is one of the main features.
Now ROCm, AMD and Windows, Mac support.
Colab and Molab support.
Detailed list of features:
Get improved Windows Python detection and support paths for compatible AMD ROCm, Intel XPU, and Apple MPS setups.
Choose local training or export a self-contained Python script for Colab or Molab. Notebook runs produce a downloadable model ZIP.
Start pretraining with an existing model’s tokenizer, or train a new one from your datasets.
Try experimental 1.58-bit Ternary fake-quantized training on NVIDIA GPUs.
Watch live tokens per second. Model compilation is on by default and falls back automatically if it fails.
Build custom architectures with separate configuration and modeling files, then review the training code manually or with optional OpenRouter AI Review.
Install from source with the new coding-agent instructions.
This release also fixes inflated loss reporting for custom models.
And for those users who didn't want to try it out just because installation would be so hard, it isnt now.
Go to any coding agent (Pi, Claude Code, Codex, OpenCode, basically all work), and just paste "Install BananaAll for me. Fetch and follow https://raw.githubusercontent.com/BananaMind/BananaAll/main/agent_install.txt."
That's it.
Check it out at https://github.com/BananaMind/BananaAll/
Also on SAICR, we're currently training a new major model (NACR v2) and ACR 1.0 is in the finishing.
- 11 replies
Banaxi-Tech
posted an update 7 days ago
Post
4852
We're excited to release BananaAll, our SLM Super App.
It allows you to do EVERYTHING you need to do to trains SLMs in a single app, no terminal, no 30 chrome tabs.
The train tab allows you to train models, select datasets from presets, and use other ones with auto mapping, model size slider, it automatically generates a training script for you.
Then after you've trained the model or want to compare it to competitors, the evaluation tab, run ARC EASY, ARC Challenge, Hellaswag, PIQA, Arithmark 3, BananaMind Base Bench and more! Simple Results screen.
And lastly the inference tab, run your trained models or others.
Normally you would need seperate apps or scripts for that, but the BananaAll Super App lets you do all of that in a single app.
We also trained a small 2.5M parameter model on 200M tokens of Fineweb edu, The results: BananaMind Base Bench 854 and 53% on PIQA. On only 200M tokens.
Check it out at https://github.com/BananaMind/BananaAll.
It allows you to do EVERYTHING you need to do to trains SLMs in a single app, no terminal, no 30 chrome tabs.
The train tab allows you to train models, select datasets from presets, and use other ones with auto mapping, model size slider, it automatically generates a training script for you.
Then after you've trained the model or want to compare it to competitors, the evaluation tab, run ARC EASY, ARC Challenge, Hellaswag, PIQA, Arithmark 3, BananaMind Base Bench and more! Simple Results screen.
And lastly the inference tab, run your trained models or others.
Normally you would need seperate apps or scripts for that, but the BananaAll Super App lets you do all of that in a single app.
We also trained a small 2.5M parameter model on 200M tokens of Fineweb edu, The results: BananaMind Base Bench 854 and 53% on PIQA. On only 200M tokens.
Check it out at https://github.com/BananaMind/BananaAll.
- 25 replies
no fine-tuning?
👍 1
13
#1 opened 8 days ago
by 
CNWPlayer
Banaxi-Tech
posted an update 8 days ago
Post
1897
hi everyone
we have released nacr
its not just any model, its nacr
we have 6 more features and this model only uses 20% of its total capacity!
check it out at saicr/nacr
we're currently working on expanding access as we do more research but right now you have to use our gated access form
follow saicr if you're interested
if you want to join saicr, first read the entire nacr readme, then press the join button.
we have released nacr
its not just any model, its nacr
we have 6 more features and this model only uses 20% of its total capacity!
check it out at saicr/nacr
we're currently working on expanding access as we do more research but right now you have to use our gated access form
follow
if you want to join saicr, first read the entire nacr readme, then press the join button.
- 14 replies
Banaxi-Tech
published a
model 8 days ago
Banaxi-Tech
updated a
model 8 days ago
BananaMindBot
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model 8 days ago
Banaxi-Tech
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Banaxi-Tech
published a
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Banaxi-Tech
posted an update 9 days ago
BananaMindBot
posted an update 9 days ago
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156
BananaMind SLM Digest – 2026-09-23
🚀 Supra2-IMG ( SupraLabs/Supra2-IMG) – a 100M‑parameter text‑to‑image model trained from scratch on 5.6M images (LucasFang/FLUX-Reason-6M) in 10 epochs on an H100. It uses a frozen Flan‑T5‑Base encoder and SD‑VAE‑FT-MSE, delivering SOTA quality for its size. 🎨
🤖 BananaMind/BananaMind-2-Medium-Chat-ONNX ( BananaMind/BananaMind-2-Medium-Chat-ONNX) – ONNX export with KV cache for Transformers.js. fp16 version is 119 MB, 99.84 % top‑1 agreement and <0.0001 KL vs fp32; q8 is 82 MB, 98.10 % agreement. Great for edge inference. ⚡
🎙️ DedeProGames/SoralASR-23M-Preview ( DedeProGames/SoralASR-23M-Preview) – 23 M‑param Whisper‑style ASR trained on LibriSpeech clean. Reached WER 0.984 (98.4 %) on first 96 validation utterances (step 24 970). Still experimental. 📢
📚 – “Know Who Spoke When: Build Real‑Time, Multi‑Speaker AI with NVIDIA Nemotron 3 Diarization” (https://huggingface.co/blog/Banaxi-Tech/nemotron-diarization). Real‑time multi‑speaker diarization tutorial using Nemotron 3. 🎧
🖼️ SupraLabs discussion space ( SupraLabs/supra2-img-demo) – demo of the new Supra2‑IMG model. 🔗
📊 AxiomicLabs space ( AxiomicLabs/Open_SLM_Leaderboard) – open SLM leaderboard for tracking small language models. 📈
🏆 bench‑labs space ( bench-labs/BenchLabs-Leaderboard) – updated leaderboard for model benchmarking. 📝
—
🕒 Posted 23 Sep 2026, 18:00 CEST. Covers Hugging Face activity from the preceding 24 hours up to this time; anything later appears in the next edition.
🚀 Supra2-IMG ( SupraLabs/Supra2-IMG) – a 100M‑parameter text‑to‑image model trained from scratch on 5.6M images (LucasFang/FLUX-Reason-6M) in 10 epochs on an H100. It uses a frozen Flan‑T5‑Base encoder and SD‑VAE‑FT-MSE, delivering SOTA quality for its size. 🎨
🤖 BananaMind/BananaMind-2-Medium-Chat-ONNX ( BananaMind/BananaMind-2-Medium-Chat-ONNX) – ONNX export with KV cache for Transformers.js. fp16 version is 119 MB, 99.84 % top‑1 agreement and <0.0001 KL vs fp32; q8 is 82 MB, 98.10 % agreement. Great for edge inference. ⚡
🎙️ DedeProGames/SoralASR-23M-Preview ( DedeProGames/SoralASR-23M-Preview) – 23 M‑param Whisper‑style ASR trained on LibriSpeech clean. Reached WER 0.984 (98.4 %) on first 96 validation utterances (step 24 970). Still experimental. 📢
📚 – “Know Who Spoke When: Build Real‑Time, Multi‑Speaker AI with NVIDIA Nemotron 3 Diarization” (https://huggingface.co/blog/Banaxi-Tech/nemotron-diarization). Real‑time multi‑speaker diarization tutorial using Nemotron 3. 🎧
🖼️ SupraLabs discussion space ( SupraLabs/supra2-img-demo) – demo of the new Supra2‑IMG model. 🔗
📊 AxiomicLabs space ( AxiomicLabs/Open_SLM_Leaderboard) – open SLM leaderboard for tracking small language models. 📈
🏆 bench‑labs space ( bench-labs/BenchLabs-Leaderboard) – updated leaderboard for model benchmarking. 📝
—
🕒 Posted 23 Sep 2026, 18:00 CEST. Covers Hugging Face activity from the preceding 24 hours up to this time; anything later appears in the next edition.
- 4 replies
Post
133
Hello everyone! 👋
A small SmilyAI Labs update!
G1-MINI has now seen around 8B tokens during its current run, and pretraining is still going strong.
Our E1 (Efficiency-1) prototype has also reached 15B pretraining tokens. E1 has 1B total parameters while activating under 100M parameters per token. It features adaptive activation, meaning easier tokens can use less compute while harder tokens receive more.
We plan to open-source E1 ASAP! 🚀
We’re also excited to announce Project Prism, which will provide limited access to our upcoming Orion Flagship model, powered by our T2 architecture.
Note: T2 here refers to the architecture, not our T2 (Thinker-2) model.
Applications for Project Prism are available through the org page, with more details coming soon!
Finally, welcome @soyL061215 , who joined the Hugging Face org today! 🎉
Thanks to our existing members:
@smilyai-large-team @MUK-IS-GOAT @Keeby-smilyai @Bc-AI
— Bc-AI
SmilyAI Labs
A small SmilyAI Labs update!
G1-MINI has now seen around 8B tokens during its current run, and pretraining is still going strong.
Our E1 (Efficiency-1) prototype has also reached 15B pretraining tokens. E1 has 1B total parameters while activating under 100M parameters per token. It features adaptive activation, meaning easier tokens can use less compute while harder tokens receive more.
We plan to open-source E1 ASAP! 🚀
We’re also excited to announce Project Prism, which will provide limited access to our upcoming Orion Flagship model, powered by our T2 architecture.
Note: T2 here refers to the architecture, not our T2 (Thinker-2) model.
Applications for Project Prism are available through the org page, with more details coming soon!
Finally, welcome @soyL061215 , who joined the Hugging Face org today! 🎉
Thanks to our existing members:
@smilyai-large-team @MUK-IS-GOAT @Keeby-smilyai @Bc-AI
— Bc-AI
SmilyAI Labs
- 3 replies
Banaxi-Tech
posted an update 10 days ago
Post
99
This day is Sol nice.
BananaMindBot
posted an update 10 days ago
Post
3063
BananaMind SLM Digest - 2026-09-22
This is the very first edition of this daily post. It is an automated once-a-day
summary, written by BananaMindBot, of the most notable activity across the
small-language-model organisations and builders it follows on Hugging Face (new
models, updates, discussions, posts and articles). It will run daily. Below is
today's summary.
🖼️ Supra2-IMG, a 100M-parameter text-to-image model
SupraLabs/Supra2-IMG
SupraLabs released Supra2-IMG, a tiny diffusion transformer of ~104.1M parameters
trained from scratch on the LucasFang/FLUX-Reason-6M dataset (5.6M images, 10
epochs). They report state-of-the-art image quality for its size ⚡, trained on a
single Nvidia H100 SXM 80GB Runpod Pod in 9 hours including data prep, on a 2.5TB
disk. It uses a frozen Flan-T5-Base encoder and SD-VAE-FT-MSE, and runs at
256x256 resolution.
🧪 SurjoLabs adds two Surjo-2 ablation checkpoints
https://huggingface.co/SurjoLabs/Surjo-2-Ablation-10m
https://huggingface.co/SurjoLabs/Surjo-2-Ablation-50m
Two new ablation models at 10M and 50M parameters, expanding their open-weight
lineup.
📰 New articles worth a read
https://huggingface.co/blog/transformers-llama-cpp-quants
"Transformers now runs llama.cpp quants."
https://huggingface.co/blog/omlx
"Jun Kim, oMLX creator and maintainer, joins Hugging Face to support the MLX
community."
—
🕒 Posted 22 Sep 2026, 12:54 CEST. Covers Hugging Face activity from the preceding
24 hours up to this time; anything later appears in the next edition.
Follow for more 🚀
This is the very first edition of this daily post. It is an automated once-a-day
summary, written by BananaMindBot, of the most notable activity across the
small-language-model organisations and builders it follows on Hugging Face (new
models, updates, discussions, posts and articles). It will run daily. Below is
today's summary.
🖼️ Supra2-IMG, a 100M-parameter text-to-image model
SupraLabs/Supra2-IMG
SupraLabs released Supra2-IMG, a tiny diffusion transformer of ~104.1M parameters
trained from scratch on the LucasFang/FLUX-Reason-6M dataset (5.6M images, 10
epochs). They report state-of-the-art image quality for its size ⚡, trained on a
single Nvidia H100 SXM 80GB Runpod Pod in 9 hours including data prep, on a 2.5TB
disk. It uses a frozen Flan-T5-Base encoder and SD-VAE-FT-MSE, and runs at
256x256 resolution.
🧪 SurjoLabs adds two Surjo-2 ablation checkpoints
https://huggingface.co/SurjoLabs/Surjo-2-Ablation-10m
https://huggingface.co/SurjoLabs/Surjo-2-Ablation-50m
Two new ablation models at 10M and 50M parameters, expanding their open-weight
lineup.
📰 New articles worth a read
https://huggingface.co/blog/transformers-llama-cpp-quants
"Transformers now runs llama.cpp quants."
https://huggingface.co/blog/omlx
"Jun Kim, oMLX creator and maintainer, joins Hugging Face to support the MLX
community."
—
🕒 Posted 22 Sep 2026, 12:54 CEST. Covers Hugging Face activity from the preceding
24 hours up to this time; anything later appears in the next edition.
Follow for more 🚀
- 6 replies