Post
24
Please give a follow to 
OrionLLM
We are conducting extensive research to build the best local models for agentic coding.
We are conducting extensive research to build the best local models for agentic coding.
DedeProGames PRO
DedeProGames
AI & ML interests
Thinking and Agentic Finetuning
Recent Activity
new activity about 3 hours ago
BananaMind/BananaMind-SLM-Leaderboard:Please benchmark and add Kiyo-135M-0960 new activity about 3 hours ago
DedeProGames/Kiyo-135M-0960:Hi. new activity about 4 hours ago
BananaMind/BananaMindBench-Leaderboard:Please benchmark and add Kiyo-135M-0960Organizations
reacted to their post with π€ about 12 hours ago
reacted to their post with π about 20 hours ago
posted an update about 20 hours ago
replied to Banaxi-Tech's post 1 day ago
What is ACR 1.0?
replied to their post 5 days ago
Im reposting it!
posted an update 5 days ago
Post
115
π Introducing the GRM-3.2 Family
The GRM-3.2 family is a new generation of reasoning-focused models from OrionLLM, purpose-built for long-horizon agentic tasks, extremely difficult reasoning problems, advanced coding, and local AI workflows across a wide range of hardware constraints.
GRM-3.2-Sky is the flagship model in the family: a 35B-A3B Mixture-of-Experts model built on the Ornith-1.0-35B architecture, designed for elite structured reasoning, complex multi-file coding, advanced mathematics, and sustained coherence across extended agentic workflows. It represents a substantial leap in long-horizon task capability over its predecessor, GRM-2.6-Plus.
GRM-3.2-Cliff is the mid-sized workhorse: a 9B-parameter model optimized for long-horizon agentic tasks and difficult reasoning in low-to-mid GPU environments. It delivers strong multi-step planning, debugging, and terminal-agent performance without demanding flagship-level hardware.
GRM-3.2-Turf is the lightweight edge model: a 1.2B-parameter model based on the LiquidAI/LFM2.5-1.2B-Thinking architecture, engineered for efficient on-device execution, high-fidelity instruction following, and robust tool use on mobile, embedded, and other resource-constrained hardware.
All three models are designed for users who need dependable reasoning engines that can maintain goal-directed behavior, planning quality, and task fidelity across many stepsβwhether on a server, a local workstation, or an edge device.
Models:
GRM-3.2-Sky: OrionLLM/GRM-3.2-Sky
GRM-3.2-Cliff: OrionLLM/GRM-3.2-Cliff
GRM-3.2-Turf: OrionLLM/GRM-3.2-Turf
Organization:
OrionLLM
The GRM-3.2 family is a new generation of reasoning-focused models from OrionLLM, purpose-built for long-horizon agentic tasks, extremely difficult reasoning problems, advanced coding, and local AI workflows across a wide range of hardware constraints.
GRM-3.2-Sky is the flagship model in the family: a 35B-A3B Mixture-of-Experts model built on the Ornith-1.0-35B architecture, designed for elite structured reasoning, complex multi-file coding, advanced mathematics, and sustained coherence across extended agentic workflows. It represents a substantial leap in long-horizon task capability over its predecessor, GRM-2.6-Plus.
GRM-3.2-Cliff is the mid-sized workhorse: a 9B-parameter model optimized for long-horizon agentic tasks and difficult reasoning in low-to-mid GPU environments. It delivers strong multi-step planning, debugging, and terminal-agent performance without demanding flagship-level hardware.
GRM-3.2-Turf is the lightweight edge model: a 1.2B-parameter model based on the LiquidAI/LFM2.5-1.2B-Thinking architecture, engineered for efficient on-device execution, high-fidelity instruction following, and robust tool use on mobile, embedded, and other resource-constrained hardware.
All three models are designed for users who need dependable reasoning engines that can maintain goal-directed behavior, planning quality, and task fidelity across many stepsβwhether on a server, a local workstation, or an edge device.
Models:
GRM-3.2-Sky: OrionLLM/GRM-3.2-Sky
GRM-3.2-Cliff: OrionLLM/GRM-3.2-Cliff
GRM-3.2-Turf: OrionLLM/GRM-3.2-Turf
Organization:
- 2 replies
reacted to appvoid's post with ππ₯π 9 days ago
Post
3850
We got gpt6 before gta6
- 15 replies
posted an update 13 days ago
Post
123
Possible Kiyo sizes
Kiyo-230M (Kiyo-Ultra)
Kiyo-135M (Kiyo-Plus)
Kiyo-65M (Kiyo-Go)
Kiyo-15M (Kiyo-Air)
Kiyo-2M (Kiyo-Pico)
Kiyo-230M (Kiyo-Ultra)
Kiyo-135M (Kiyo-Plus)
Kiyo-65M (Kiyo-Go)
Kiyo-15M (Kiyo-Air)
Kiyo-2M (Kiyo-Pico)
reacted to Banaxi-Tech's post with π€π₯ 14 days ago
Post
2894
We're excited to release BananaMind 2.1 Pico Preview!
It includes the first preview of our BananaMind 2.1 architecture!
This model gets near BananaMind 2 Micro performance at half the parameters and 37.5x less tokens!
Thats insane!
The current architectures includes about 500K parameters of the total 1.5M parameters in n-gram embeddings and the layer 2 is run twice.
It also includes XSA and the XSA refresh gate.
We're still going to improve the architecture in the final release.
Check it out at:
Follow us for more models:
BananaMind
@Banaxi-Tech
@vovaRL
@DedeProGames
It includes the first preview of our BananaMind 2.1 architecture!
This model gets near BananaMind 2 Micro performance at half the parameters and 37.5x less tokens!
Thats insane!
The current architectures includes about 500K parameters of the total 1.5M parameters in n-gram embeddings and the layer 2 is run twice.
It also includes XSA and the XSA refresh gate.
We're still going to improve the architecture in the final release.
Check it out at:
Follow us for more models:
@Banaxi-Tech
@vovaRL
@DedeProGames
- 3 replies
replied to KlondikeDev's post 14 days ago
Give me the seahorse emoji!
Here is it: π‘
Wait thats the wrong one, here is the seahorse emoji: π>π¦>π΅> π>π¦>π΅> π>π¦>π΅> π>π¦>π΅> π>π¦>π΅> π>π¦>π΅> π>π¦>π΅> π>π¦>π΅> π>π¦>π΅> π>π¦>π΅> π>π¦>π΅>
Just kidding! HERE IS IT: π‘
replied to Banaxi-Tech's post 16 days ago
Yo @Banaxi-Tech im training CodeMax-8M
reacted to OppaAI's post with π 16 days ago
Post
2702
Congrat to HuggingFace Team
https://techcrunch.com/2026/08/26/nvidia-closes-in-on-hugging-face-acquisition/
https://techcrunch.com/2026/08/26/nvidia-closes-in-on-hugging-face-acquisition/
- 3 replies
reacted to etemiz's post with ππ₯ 17 days ago
Post
561
fine tuning going well, without breaking the model
- 1 reply
I need Haiku 5
replied to Banaxi-Tech's post about 1 month ago
Yay!
reacted to Banaxi-Tech's post with π about 1 month ago
Post
2757
Today we wanted to release BananaMind 2 Pico, our smallest model yet at ~0.9M parameters. Instead, we accidentally ran a very expensive experiment on what happens when you push a tiny model way past its useful token budget.
Short version: we trained on 200B tokens (~222K:1 tokens-per-parameter). The model peaked at 20B tokens with an INT Index of 4.55, then degraded monotonically over the next 160B to 3.31 β a 27% regression. Three of four Open SLM benchmarks were worse at the end of training than they were at 10% through.
The useful compute-optimal range for Pico-tier models looks like ~22Kβ30K tokens per parameter. Ratios like 7K:1, 15K:1, and 22K:1 all work fine β TinyStories and most sub-3M community models sit in this range. Push much further and benchmarks start rotting.
Follow us for more:
BananaMind
@vovaRL
@Banaxi-Tech
Full writeup with all checkpoints, the Chinchilla-ratio control run, and the schedule-vs-overtraining analysis: https://huggingface.co/blog/Banaxi-Tech/ovdadadadd
And if anyone, i dont know the reason why you would, wants the 20B token checkpoint reply and ill upload it as BananaMind 2.1 Pico EXP
Short version: we trained on 200B tokens (~222K:1 tokens-per-parameter). The model peaked at 20B tokens with an INT Index of 4.55, then degraded monotonically over the next 160B to 3.31 β a 27% regression. Three of four Open SLM benchmarks were worse at the end of training than they were at 10% through.
The useful compute-optimal range for Pico-tier models looks like ~22Kβ30K tokens per parameter. Ratios like 7K:1, 15K:1, and 22K:1 all work fine β TinyStories and most sub-3M community models sit in this range. Push much further and benchmarks start rotting.
Follow us for more:
@vovaRL
@Banaxi-Tech
Full writeup with all checkpoints, the Chinchilla-ratio control run, and the schedule-vs-overtraining analysis: https://huggingface.co/blog/Banaxi-Tech/ovdadadadd
And if anyone, i dont know the reason why you would, wants the 20B token checkpoint reply and ill upload it as BananaMind 2.1 Pico EXP
- 3 replies