Oh my we're playing it a little loose and fast with that "mini" adjective aren't we. Here I was expecting something I could run an atlas on π«€
R PRO
juiceb0xc0de
AI & ML interests
Mechanistic interpretation
SAE weight training
Novel architecture exploration
Brain atlas builds
Recent Activity
liked a Space about 3 hours ago
juiceb0xc0de/b-sides-v2 new activity about 8 hours ago
juiceb0xc0de/llama-cpp-cuda128-blackwell120:whats this?Organizations
replied to their post about 10 hours ago
Thank you I appreciate it.
reacted to Banaxi-Tech's post with π about 22 hours ago
Post
2111
We're going to release our BananaMind 2.1 models very soon!
We're also announcing 2 new models.
All of our models we will train are:
BananaMind 2.1 Flash Lite, 10M parameters with 8M in transformer and 2M in n-gram. 50B pretraining tokens.
BananaMind 2.1 Lite with 25M parameters, 5M in n-gram and 20M in transformer. 75B pretraining tokens.
BananaMind 2.1 Flash with 50M parameters, with undecided n-gram count yet. 100B pretraining tokens.
BananaMind 2.1 Pro with 145M parameters, with undecided n-gram count yet. 150-200B pretraining tokens.
BananaMind 2.1 Coder with 149M parameters with undecided n-gram count yet.
We're now announcing BananaMind 2.1 NanoCoder, a 10M parameter model focused specifically on coding and BananaMind 2.1 MiniCoder which is a 25M parameter model focused on coding.
Follow us:
BananaMind
@Banaxi-Tech
@vovaRL
@DedeProGames
bananamind-research-community
We're also announcing 2 new models.
All of our models we will train are:
BananaMind 2.1 Flash Lite, 10M parameters with 8M in transformer and 2M in n-gram. 50B pretraining tokens.
BananaMind 2.1 Lite with 25M parameters, 5M in n-gram and 20M in transformer. 75B pretraining tokens.
BananaMind 2.1 Flash with 50M parameters, with undecided n-gram count yet. 100B pretraining tokens.
BananaMind 2.1 Pro with 145M parameters, with undecided n-gram count yet. 150-200B pretraining tokens.
BananaMind 2.1 Coder with 149M parameters with undecided n-gram count yet.
We're now announcing BananaMind 2.1 NanoCoder, a 10M parameter model focused specifically on coding and BananaMind 2.1 MiniCoder which is a 25M parameter model focused on coding.
Follow us:
@Banaxi-Tech
@vovaRL
@DedeProGames
- 5 replies
replied to their post 1 day ago
Dropping soon! Embedded:
- Kernel data
- Docker Images
- MCP Servers
- Buckets
- Niche collections
New tools:
- Search exclisions
- Parameter sliders
- Layer digest similarity
- Copy-to-run
- Verified only toggle
posted an update 2 days ago
Post
3074
Hey I've updated my Hugging Face text generation model search space B-Sides. I always wanted more from HF's model search, so I built one.
I went deeper than the model card, embedding the relevant .json and .py files so you can search for models with custom kernels or exotic imports and specific architecture shapes. You can narrow it down to quantization types and training stacks. If you want to search it and it's not available just make a community post and I would gladly make each query more detailed with an update.
So far over 428,440 text generation models are catalogued with more coming weekly.
On deck: Docker images
https://huggingface.co/spaces/juiceb0xc0de/b-sides
I went deeper than the model card, embedding the relevant .json and .py files so you can search for models with custom kernels or exotic imports and specific architecture shapes. You can narrow it down to quantization types and training stacks. If you want to search it and it's not available just make a community post and I would gladly make each query more detailed with an update.
So far over 428,440 text generation models are catalogued with more coming weekly.
On deck: Docker images
https://huggingface.co/spaces/juiceb0xc0de/b-sides
- 4 replies
reacted to Bc-AI's post with π 3 days ago
Post
2507
Building a 6.58B sparse MoE model from scratch on a single GPU.
Hey everyone! Today is day 1 of training Smilyai-Lab's new model I call G1-MINI. It's basically the smaller version of our planned model G1 which will be 20B and activate about 2B per token. MINI activates about 1.16B params per token and is currently training right now. If no errors spring up now, I'd say i can launch sometime around September 15th~ish. Thanks to our beta testers:
@guardamarcos
@ProCreations
@juiceb0xc0de
@Timmy6767
@Sbui503
@atom77777
@Fishtiks
@smartdigitalnetworks
@EmetTheGolum
@smilyai-large-team
@MUK-IS-GOAT
@Bc-AI
@Banaxi-Tech
@vovaRL
@Datdanboi25
Hey everyone! Today is day 1 of training Smilyai-Lab's new model I call G1-MINI. It's basically the smaller version of our planned model G1 which will be 20B and activate about 2B per token. MINI activates about 1.16B params per token and is currently training right now. If no errors spring up now, I'd say i can launch sometime around September 15th~ish. Thanks to our beta testers:
@guardamarcos
@ProCreations
@juiceb0xc0de
@Timmy6767
@Sbui503
@atom77777
@Fishtiks
@smartdigitalnetworks
@EmetTheGolum
@smilyai-large-team
@MUK-IS-GOAT
@Bc-AI
@Banaxi-Tech
@vovaRL
@Datdanboi25
- 9 replies
reacted to Bc-AI's post with π 7 days ago
Post
2597
Smilyai News
Hello everyone! August has been a crazy month for us at Smilyai-Labs. We've been doing lots behind the scenes, so here's the latest π
1. MiniCoder
We are very close to releasing MiniCoder-1, our first-generation coding model designed for reasoning and coding. Our planned context window is 128K, but the earlier versions probably will not support that long! It's currently in the final stages of DPO so expect a release in early september.
Release: VERY SOONβ’π€£
2. Smilyai G1
So, the current plan is 20B parameter model total, with a MoE architecture, activating around 2B parameters per token. Its desgigned for maximum performance but keeping it runnable on consumer hardware. It's only a plan and i have no idea when me and the team can finish it. Expect a launch around the end of september to early october-ish. I have no guarantees so don't quote me on the launch date.
3. T1
Smilyai-T1 is another major model we are working on.
The goal for T1 is to take what we learnt from the countless architectural experiments and creating a powerful model designed for thinking. Think MiniCoder but reasons more and G1 but more capable. Its main goals are coding, math, reasoning and general capability.
4. Omni
We are also planning Omni, our first from scratch multimodal model. It will not launch this year as it will take a while. We are actively researching the best architecture for it and we will update progress as we go!
Thanks to our beta testers:
@guardamarcos
@ProCreations
@juiceb0xc0de
@Timmy6767
@Sbui503
@atom77777
@Fishtiks
@smartdigitalnetworks
@EmetTheGolum
@smilyai-large-team
@MUK-IS-GOAT
@Bc-AI
Thanks to my friends who work with me at lunchtimes (Smilyai-Labs team):
@MUK-IS-GOAT
@smilyai-large-team
August was wild. Letβs see what September brings. π
β Bc-AI, on behalf of SmilyAI Labs
Hello everyone! August has been a crazy month for us at Smilyai-Labs. We've been doing lots behind the scenes, so here's the latest π
1. MiniCoder
We are very close to releasing MiniCoder-1, our first-generation coding model designed for reasoning and coding. Our planned context window is 128K, but the earlier versions probably will not support that long! It's currently in the final stages of DPO so expect a release in early september.
Release: VERY SOONβ’π€£
2. Smilyai G1
So, the current plan is 20B parameter model total, with a MoE architecture, activating around 2B parameters per token. Its desgigned for maximum performance but keeping it runnable on consumer hardware. It's only a plan and i have no idea when me and the team can finish it. Expect a launch around the end of september to early october-ish. I have no guarantees so don't quote me on the launch date.
3. T1
Smilyai-T1 is another major model we are working on.
The goal for T1 is to take what we learnt from the countless architectural experiments and creating a powerful model designed for thinking. Think MiniCoder but reasons more and G1 but more capable. Its main goals are coding, math, reasoning and general capability.
4. Omni
We are also planning Omni, our first from scratch multimodal model. It will not launch this year as it will take a while. We are actively researching the best architecture for it and we will update progress as we go!
Thanks to our beta testers:
@guardamarcos
@ProCreations
@juiceb0xc0de
@Timmy6767
@Sbui503
@atom77777
@Fishtiks
@smartdigitalnetworks
@EmetTheGolum
@smilyai-large-team
@MUK-IS-GOAT
@Bc-AI
Thanks to my friends who work with me at lunchtimes (Smilyai-Labs team):
@MUK-IS-GOAT
@smilyai-large-team
August was wild. Letβs see what September brings. π
β Bc-AI, on behalf of SmilyAI Labs
reacted to Banaxi-Tech's post with π 8 days ago
Post
2871
Weβre excited to release BananaMind OS 2.0, a major update to our portable operating system for running AI models locally.
BananaMind OS runs directly from an ISO without Linux, installation, a cloud connection, or modifying your disks.
The new Version 2.0 adds a graphical interface with mouse support, a model library, multi-turn chat, configurable KV cache, maximum tokens and temperature
controls, automatic x87/SSE/SSE2 CPU detection, BIOS support, native UEFI support and a dedicated 486 compatibility mode.
Model weights are not loaded during GRUB or startup. Only the model catalog is read, and the selected model is loaded after BananaMind OS starts.
We now have a new .litemodel format supports multiple architectures, including BananaMind models, SmolLM, SmolLM2, GPT-X2.5, min-spark 1.1 and Rose-Mini.
Check it out at:
https://github.com/BananaMind/BananaMindOS
You can build an ISO yourself using the .sh or .bat script. Select the models and quantizations you want, and the builder will download, quantize
and package them locally.
Prebuilt 10MB, 25MB, 100MB and 250MB model presets are available here:
https://github.com/BananaMind/BananaMindOS/releases/tag/v2.0.0
Use the regular preset ISOs for BIOS and GRUB, including the 486 compatibility mode.
Warning: Im currently uploading the ISOs, all up to 100MB is present, 250MB is getting uploaded
Use the files ending in -uefi.iso for the native x86-64 UEFI graphical frontend and improved firmware mouse support.
The preset name describes the RAM class of the individual included models. Models remain on the ISO until selected, so including multiple models does not load all of them into RAM.
486DX with an x87 FPU, Pentium and newer x86 processors are supported. Modern x86-64 computers are supported through UEFI. It currently doesent support processors without an FPU.
Comment if you want me to run it on 0.04MHz (pls dont)
Video Credit:
Song: Matzan - Redesigned
Music provided by NoCopyrightSounds
BananaMind OS runs directly from an ISO without Linux, installation, a cloud connection, or modifying your disks.
The new Version 2.0 adds a graphical interface with mouse support, a model library, multi-turn chat, configurable KV cache, maximum tokens and temperature
controls, automatic x87/SSE/SSE2 CPU detection, BIOS support, native UEFI support and a dedicated 486 compatibility mode.
Model weights are not loaded during GRUB or startup. Only the model catalog is read, and the selected model is loaded after BananaMind OS starts.
We now have a new .litemodel format supports multiple architectures, including BananaMind models, SmolLM, SmolLM2, GPT-X2.5, min-spark 1.1 and Rose-Mini.
Check it out at:
https://github.com/BananaMind/BananaMindOS
You can build an ISO yourself using the .sh or .bat script. Select the models and quantizations you want, and the builder will download, quantize
and package them locally.
Prebuilt 10MB, 25MB, 100MB and 250MB model presets are available here:
https://github.com/BananaMind/BananaMindOS/releases/tag/v2.0.0
Use the regular preset ISOs for BIOS and GRUB, including the 486 compatibility mode.
Warning: Im currently uploading the ISOs, all up to 100MB is present, 250MB is getting uploaded
Use the files ending in -uefi.iso for the native x86-64 UEFI graphical frontend and improved firmware mouse support.
The preset name describes the RAM class of the individual included models. Models remain on the ISO until selected, so including multiple models does not load all of them into RAM.
486DX with an x87 FPU, Pentium and newer x86 processors are supported. Modern x86-64 computers are supported through UEFI. It currently doesent support processors without an FPU.
Comment if you want me to run it on 0.04MHz (pls dont)
Video Credit:
Song: Matzan - Redesigned
Music provided by NoCopyrightSounds
- 3 replies
posted an update 9 days ago
Post
118
Just dropped a new SAE juiceb0xc0de/gemma-4-e2b-it-SAE
I built this using the Event-Aware SAE Trainer I have been developing. It trained all 35 layers in 1 run and handled the transition from KV owning layers to the KV cache at layers 15 and 16 where MLP width doubles.
I developed the trainer with two goals in mind, keep it accessible and affordable. I wanted to make SAE training simple enough that you don't require a team of frontier developers or a corporate budget. Anyone can choose a model -> hit play -> train a set of SAE's. Zero hyperparameter tweaking. Train every layer.
Optimized features:
- Single block rolling capture approx. 2.9x faster vs full forwards capture zero measured relative error
- Hoist system the model lives in CPU the layers required for SAE training stay in VRAM
- Augmented-Lagrangian Ξ» controller with two sided nudge controller for faster, bidirectional L=0 control
- Dead feature revival preventing dead pileup saving layers from requiring another pass
- Layer-aware early stop triggers prevent training from executing maximum training steps dropping training as low as recorded 1000 steps
juiceb0xc0de/gemma-4-e2b-it-SAE
https://github.com/JuiceB0xC0de/event-aware-SAE-trainer
Drop a new discussion in the community and let me know what you think!
I built this using the Event-Aware SAE Trainer I have been developing. It trained all 35 layers in 1 run and handled the transition from KV owning layers to the KV cache at layers 15 and 16 where MLP width doubles.
I developed the trainer with two goals in mind, keep it accessible and affordable. I wanted to make SAE training simple enough that you don't require a team of frontier developers or a corporate budget. Anyone can choose a model -> hit play -> train a set of SAE's. Zero hyperparameter tweaking. Train every layer.
Optimized features:
- Single block rolling capture approx. 2.9x faster vs full forwards capture zero measured relative error
- Hoist system the model lives in CPU the layers required for SAE training stay in VRAM
- Augmented-Lagrangian Ξ» controller with two sided nudge controller for faster, bidirectional L=0 control
- Dead feature revival preventing dead pileup saving layers from requiring another pass
- Layer-aware early stop triggers prevent training from executing maximum training steps dropping training as low as recorded 1000 steps
juiceb0xc0de/gemma-4-e2b-it-SAE
https://github.com/JuiceB0xC0de/event-aware-SAE-trainer
Drop a new discussion in the community and let me know what you think!
reacted to GoktugD's post with π 9 days ago
Post
1991
πΉπ· Introducing T3 Gemstone β Edge AI & Cybersecurity Models from TΓΌrkiye
We've been building an open AI ecosystem focused on practical models that can run closer to the edge β not only in large datacenters.
Today, I'm introducing T3 Gemstone, a growing family of compact AI models built around edge inference, cybersecurity and computer vision.
π T3 Gemstone currently includes:
* NanoSOC Gemstone 2B β GGUF
* NanoSOC Gemstone 4B β GGUF
* Gemstone Person/Object Detector Nano
* Edge-focused AI experiments and deployments
The goal is simple:
Build smaller, practical and open AI systems that can actually run on constrained hardware.
This is part of a much larger open-source effort we're building from TΓΌrkiye across LLMs, cybersecurity, computer vision, retrieval, speech and edge AI.
There is much more coming.
π€ Explore my models, datasets and demos:
@GoktugD
π‘οΈ NanoSOC:
Werea-co/Werea-NanoSOC-8B
Feedback, benchmarks, collaborations and contributions are very welcome.
If you're interested inopen-source AI, Turkish AI research, edge AI or cybersecurity models, follow the journey.
We're just getting started. πΉπ·
#AI #OpenSource #HuggingFace #LLM #EdgeAI #Cybersecurity #ComputerVision #TurkishAI #MachineLearning
We've been building an open AI ecosystem focused on practical models that can run closer to the edge β not only in large datacenters.
Today, I'm introducing T3 Gemstone, a growing family of compact AI models built around edge inference, cybersecurity and computer vision.
π T3 Gemstone currently includes:
* NanoSOC Gemstone 2B β GGUF
* NanoSOC Gemstone 4B β GGUF
* Gemstone Person/Object Detector Nano
* Edge-focused AI experiments and deployments
The goal is simple:
Build smaller, practical and open AI systems that can actually run on constrained hardware.
This is part of a much larger open-source effort we're building from TΓΌrkiye across LLMs, cybersecurity, computer vision, retrieval, speech and edge AI.
There is much more coming.
π€ Explore my models, datasets and demos:
@GoktugD
π‘οΈ NanoSOC:
Werea-co/Werea-NanoSOC-8B
Feedback, benchmarks, collaborations and contributions are very welcome.
If you're interested inopen-source AI, Turkish AI research, edge AI or cybersecurity models, follow the journey.
We're just getting started. πΉπ·
#AI #OpenSource #HuggingFace #LLM #EdgeAI #Cybersecurity #ComputerVision #TurkishAI #MachineLearning
reacted to Bc-AI's post with π 9 days ago
Post
2011
Hello everyone! Happy to say that MiniCoder-1 is now in the instruction tuning phase. It is a 216M~ parameter model trained on 16B tokens. It ran on an RTX 6000 Pro Blackwell gpu for around 24~ hours. Now we will do SFT and launch as beta while we work on the final important DPO and RLHF phases. Our goal is a small extreemly fast on device coding assistant with CoT reasoning* baked in! - Bc-AI on behalf of the Smilyai-Labs team
*it is a small model so the reasoning quality wont be as good obviously!
*it is a small model so the reasoning quality wont be as good obviously!
- 2 replies
replied to their post 10 days ago
juiceb0xc0de/qwen3.8-27b-atlas
My apologies for the slow turn around on this one. I got distracted and tried to re-version the atlas building suite I use. Next thing I knew 2 days had gone by, I was knee deep in debugging and I was back to bartending an event weekend with no free time. I made this one queryable in the repo. Hope you like it.
@Banaxi-Tech I haven't forgotten about you. Im just catching up a bunch of projects right now. I'll get your model finished up right away here.
reacted to Bc-AI's post with π€ 12 days ago
Post
3781
New update! We are currently training a few new models now! Our 3rd generation main LLM standard edition is in training right now. We are also training a new LLM line called Tiny Coder around 350~ish M params. Thanks to @Banaxi-Tech for inspiring the architecture with his Bananamind-2.1-unified test model. Thanks to our beta testers: @juiceb0xc0de @ProCreations @Sbui503 @Fishtiks @MUK-IS-GOAT
- 4 replies
What are they feeding you in that school? Obscure LLM data and ancient MS DOS tomes?
As always I cant wait to see what kind of hidden conflict your unpredictable and chaotic architecture has in store for me while configuring my mech-interp suite.
reacted to Bc-AI's post with π 15 days ago
Post
4145
@juiceb0xc0de @ProCreations @Keeby-smilyai @MUK-IS-GOAT @Sbui503 @Fishtiks Hello Beta testers! I will release our new model in our beta testers organisation as soon as I can! Thanks for being part of our beta testers community!
- 7 replies
replied to their post 15 days ago
The model is downloading right now. It should take a couple of hours and it will be done
replied to their post 15 days ago
Ya I got you. Give me a bit to set up up here and I'll get it started right away.
replied to danielhanchen's post 23 days ago
Hey just a heads up you split your unsl/oth link accidentally. π¦₯
reacted to danielhanchen's post with π 23 days ago
Post
5628
Introducing Unsloth Desktop π¦₯
The first desktop app to run and train models locally.
β’ Open-source. Runs on Mac, Windows and Linux
β’ Supports MLX, diffusion image/video, audio, GGUF
β’ Connect Claude Code and Codex to local LLMs
β’ 50% more accurate, self-healing tool calls + sandboxed code exec
β’ Works for CPU + multiGPU setups - NVIDIA, AMD, Intel, Mac
β’ Train models 2Γ faster with 70% less VRAM
β’ Private web search, deep research, RAG, MCP and exports (NVFP4, GGUF)
β’ Use Unslothβs OpenAI-compatible API and cloud models
β’ Securely deploy LLMs remotely and access anywhere
Unsloth Desktop is now available on http://unsloth.ai
and GitHub.
GitHub: https://github.com/unslothai/unsloth
Blog and Guide: https://unsloth.ai/docs/desktop
The first desktop app to run and train models locally.
β’ Open-source. Runs on Mac, Windows and Linux
β’ Supports MLX, diffusion image/video, audio, GGUF
β’ Connect Claude Code and Codex to local LLMs
β’ 50% more accurate, self-healing tool calls + sandboxed code exec
β’ Works for CPU + multiGPU setups - NVIDIA, AMD, Intel, Mac
β’ Train models 2Γ faster with 70% less VRAM
β’ Private web search, deep research, RAG, MCP and exports (NVFP4, GGUF)
β’ Use Unslothβs OpenAI-compatible API and cloud models
β’ Securely deploy LLMs remotely and access anywhere
Unsloth Desktop is now available on http://unsloth.ai
and GitHub.
GitHub: https://github.com/unslothai/unsloth
Blog and Guide: https://unsloth.ai/docs/desktop
- 6 replies