I want to see BananaMindV 🔥
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DedeProGames
AI & ML interests
Thinking and Agentic Finetuning
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replied to Banaxi-Tech's post 1 day ago
reacted to Banaxi-Tech's post with 😎🤗🔥 1 day ago
Post
2316
We're announcing BananaMind 2 Micro, our smallest model in the BananaMind 2 model family.
This model is not released yet, training has not started yet.
It uses only 2.9M parameters, while being overtrained on 75B tokens to get the maximum intelligence per parameter.
The key changes are:
No more AdamW, the model will use the Muon optimizer offering up to 2x faster convergence and higher lr.
LR goes to 2.2e-2.
We're adding the XSA refresh gate from the TX4 architecture into our own.
Training will start on August 3, release date is estimated to be August 4-6.
On August 3 we will also release our Public Preview of BananaMind 2 Pro.
Follow us to know when our models release
BananaMind
@Banaxi-Tech
This model is not released yet, training has not started yet.
It uses only 2.9M parameters, while being overtrained on 75B tokens to get the maximum intelligence per parameter.
The key changes are:
No more AdamW, the model will use the Muon optimizer offering up to 2x faster convergence and higher lr.
LR goes to 2.2e-2.
We're adding the XSA refresh gate from the TX4 architecture into our own.
Training will start on August 3, release date is estimated to be August 4-6.
On August 3 we will also release our Public Preview of BananaMind 2 Pro.
Follow us to know when our models release
@Banaxi-Tech
- 9 replies
posted an update 1 day ago
Post
98
🚀 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 steps—whether 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 steps—whether 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:
reacted to ProCreations's post with 🤝😔🚀😎🔥🤗➕🤯❤️👀🧠👍 5 days ago
Post
2054
what should i do for 200 followers... hmmmm... top reply to this post (most emojis) wins, just not anything too crazy
- 11 replies
reacted to Banaxi-Tech's post with 👀🔥 8 days ago
Post
3606
We're excited to announce BananaMind 2V, our small vision model series!
These models are NOT released yet.
We will release them in mid-august!
BananaMind 2V will include:
BananaMind 2V 256M, the flagship based on BananaMind 2 Pro (BananaMind 2 Pro is not released yet).
BananaMind 2V 100M, our mid model, based on BananaMind 2 Medium.
BananaMind 2V 50M, our smallest vision model, based on BananaMind 2 Mini.
These are currently unreleased and will release in mid-august.
Our training will start after BananaMind 2 Pro has finished training.
These models are NOT released yet.
We will release them in mid-august!
BananaMind 2V will include:
BananaMind 2V 256M, the flagship based on BananaMind 2 Pro (BananaMind 2 Pro is not released yet).
BananaMind 2V 100M, our mid model, based on BananaMind 2 Medium.
BananaMind 2V 50M, our smallest vision model, based on BananaMind 2 Mini.
These are currently unreleased and will release in mid-august.
Our training will start after BananaMind 2 Pro has finished training.
reacted to vineeth98's post with 👍 9 days ago
Post
881
I made a speedrun leaderboard for LoRA fine-tuning. One frozen task (Qwen2.5-1.5B to 57% on GSM8K), one GPU, fastest training run wins. Every record gets re-run 3x with fresh seeds on identical hardware before it counts, so no self-reported numbers.
The baseline was 11:57 three days ago. Someone already got it down to 1:44, with data pruning and a chunked cross-entropy that never materializes the logits.
Attempting is free (Modal's monthly credits cover full runs), and the second track (SmolLM2 + SQuAD) is still sitting at its naive baseline — easy first record for someone.
vineeth98/lora-speedrun
The baseline was 11:57 three days ago. Someone already got it down to 1:44, with data pruning and a chunked cross-entropy that never materializes the logits.
Attempting is free (Modal's monthly credits cover full runs), and the second track (SmolLM2 + SQuAD) is still sitting at its naive baseline — easy first record for someone.
vineeth98/lora-speedrun
- 7 replies