Good suggestions, I'll see what I can come up with.
Lee Miller PRO
dleemiller
AI & ML interests
Cross encoders, NLI, retrieval, computer vision
Recent Activity
repliedto their post about 13 hours 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 π₯°), but LLMs are welcome too!
https://huggingface.co/spaces/dleemiller/open-nli-leaderboard repliedto their post about 13 hours 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 π₯°), but LLMs are welcome too!
https://huggingface.co/spaces/dleemiller/open-nli-leaderboard updated a Space 1 day ago
dleemiller/open-nli-leaderboardOrganizations
replied to their post about 13 hours ago
replied to their post about 13 hours ago
I've trained the ettin 17m model, but that's still a bidirectional model. I am working on training larger causal models right now, but I can give a tiny one a shot! I have a 10M param model I've trained on about 50B tokens but I haven't done anything with it yet.
posted an update 2 days ago
Post
2,342
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 π₯°), but LLMs are welcome too!
dleemiller/open-nli-leaderboard
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 π₯°), but LLMs are welcome too!
dleemiller/open-nli-leaderboard
- 4 replies
reacted to Banaxi-Tech's post with β€οΈ 2 days ago
Post
7,642
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 modelβs 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 modelβs 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
reacted to tomaarsen's post with β€οΈ about 1 year ago
Post
4,760
π€ Sentence Transformers is joining Hugging Face! π€ This formalizes the existing maintenance structure, as I've personally led the project for the past two years on behalf of Hugging Face! Details:
Today, the Ubiquitous Knowledge Processing (UKP) Lab is transferring the project to Hugging Face. Sentence Transformers will remain a community-driven, open-source project, with the same open-source license (Apache 2.0) as before. Contributions from researchers, developers, and enthusiasts are welcome and encouraged. The project will continue to prioritize transparency, collaboration, and broad accessibility.
Read our full announcement for more details and quotes from UKP and Hugging Face leadership: https://huggingface.co/blog/sentence-transformers-joins-hf
We see an increasing wish from companies to move from large LLM APIs to local models for better control and privacy, reflected in the library's growth: in just the last 30 days, Sentence Transformer models have been downloaded >270 million times, second only to transformers.
I would like to thank the UKP Lab, and especially Nils Reimers and Iryna Gurevych, both for their dedication to the project and for their trust in myself, both now and two years ago. Back then, neither of you knew me well, yet you trusted me to take the project to new heights. That choice ended up being very valuable for the embedding & Information Retrieval community, and I think this choice of granting Hugging Face stewardship will be similarly successful.
I'm very excited about the future of the project, and for the world of embeddings and retrieval at large!
Today, the Ubiquitous Knowledge Processing (UKP) Lab is transferring the project to Hugging Face. Sentence Transformers will remain a community-driven, open-source project, with the same open-source license (Apache 2.0) as before. Contributions from researchers, developers, and enthusiasts are welcome and encouraged. The project will continue to prioritize transparency, collaboration, and broad accessibility.
Read our full announcement for more details and quotes from UKP and Hugging Face leadership: https://huggingface.co/blog/sentence-transformers-joins-hf
We see an increasing wish from companies to move from large LLM APIs to local models for better control and privacy, reflected in the library's growth: in just the last 30 days, Sentence Transformer models have been downloaded >270 million times, second only to transformers.
I would like to thank the UKP Lab, and especially Nils Reimers and Iryna Gurevych, both for their dedication to the project and for their trust in myself, both now and two years ago. Back then, neither of you knew me well, yet you trusted me to take the project to new heights. That choice ended up being very valuable for the embedding & Information Retrieval community, and I think this choice of granting Hugging Face stewardship will be similarly successful.
I'm very excited about the future of the project, and for the world of embeddings and retrieval at large!
- 1 reply