dleemiller/OpenNLI-Leaderboard
Updated • 239
Lee Miller PRO
dleemiller
AI & ML interests
Cross encoders, NLI, retrieval, computer vision
Recent Activity
updated a Space about 11 hours ago
dleemiller/open-nli-leaderboard posted an update 2 days ago
Join the competition!
We have 45 models scored (and growing), across 44 public benchmarks. My goal for this space is to push the limits on NLI and the breadth of tasks these models can perform in. Contributions are encouraged, as well as suggestions for additional benchmarks.
Right now, all models are cross encoders (Sentence Transformers 🥰), but LLMs are welcome too!
https://huggingface.co/spaces/dleemiller/open-nli-leaderboard reacted to Banaxi-Tech's post with ❤️ 2 days ago
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.
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