zai-org/GLM-5.3-Flash
Image-Text-to-Text • 321B • Updated • 379k • • 1.79k
🏗️ Building on HF
John Locke
johnlockejrr
AI & ML interests
OCR, HTR, ATR, NLP, AI
Recent Activity
liked a model 2 days ago
zai-org/GLM-5.3-Flash liked a model 3 days ago
zai-org/GLM-5.3 liked a model 3 days ago
tencent/Hy3Organizations
liked a
model 2 days ago
liked 2
models 3 days ago
reacted to salma-remyx's post with 👀 4 days ago
Post
2692
If you can’t explain why your AI-generated contribution belongs in the repo, don’t put it in a maintainer’s queue 🙅🏻♀️
Before asking for review, you should be able to answer:
1. What project need does it address?
2. Where does it fit, and does it duplicate existing work?
3. What evidence shows it works, and will you own it through review?
If you can’t answer those, you haven’t saved anyone time. You’ve passed the buck to the maintainer.
Outrider has made us better contributors by doing more of this work before upstream review by
* reading contribution rules, accepted PRs, and open issues
* finding needs and integration points
* drafting the code, tests, and context.
We still decide what deserves to go upstream, verify the claims, coordinate with maintainers and contributors, and stay involved through review.
On huggingface/peft, only 4 of 20 Outrider runs opened draft PRs. Two contributions have now merged:
✅ Riemannian-preconditioned LoRA: https://github.com/huggingface/peft/pull/3382
✅ Super-Tuning: https://github.com/huggingface/peft/pull/3518
We have more contributions in review and far more ideas were filtered out before they reached a maintainer.
Full case study: https://remyx.ai/case-study
Outrider: https://github.com/remyxai/outrider
Before asking for review, you should be able to answer:
1. What project need does it address?
2. Where does it fit, and does it duplicate existing work?
3. What evidence shows it works, and will you own it through review?
If you can’t answer those, you haven’t saved anyone time. You’ve passed the buck to the maintainer.
Outrider has made us better contributors by doing more of this work before upstream review by
* reading contribution rules, accepted PRs, and open issues
* finding needs and integration points
* drafting the code, tests, and context.
We still decide what deserves to go upstream, verify the claims, coordinate with maintainers and contributors, and stay involved through review.
On huggingface/peft, only 4 of 20 Outrider runs opened draft PRs. Two contributions have now merged:
✅ Riemannian-preconditioned LoRA: https://github.com/huggingface/peft/pull/3382
✅ Super-Tuning: https://github.com/huggingface/peft/pull/3518
We have more contributions in review and far more ideas were filtered out before they reached a maintainer.
Full case study: https://remyx.ai/case-study
Outrider: https://github.com/remyxai/outrider
- 3 replies
liked a
dataset 6 days ago
[UNRELATED] arXiv endorsement request (via William Mattingly)
#2 opened 9 days ago
by johnlockejrr
liked a
Space 8 days ago
updated 2
models 9 days ago
Inference code for the polyline head?
👍 1
9
#1 opened 17 days ago
by 
lazyants
reacted to SeaWolf-AI's post with 🔥 10 days ago
Post
4054
🔋 Open Materials Challenge, Season 1 — Solid-State Battery Electrolytes
A solid-state battery replaces the liquid electrolyte of a lithium-ion cell with a solid. It does not catch fire, it lasts longer, and it can hold more. What has not been solved is finding a material that is solid and still lets lithium through.
Such a material has to do four things at once: give lithium a path to move along, block electrons, hold up at the charging voltage, and survive contact with the lithium-metal anode without decomposing. Plenty of materials manage three. Very few manage all four.
This challenge looks for candidates, together. You submit one composition — for example Li3YCl6. We score it computationally and place it on the board. There is no prize.
Scoring (100 points)
Oxidation stability 40 does it resist decomposing as the voltage rises
Lithium-metal stability 35 does it survive contact with the anode
Use novelty 25 higher if it has not been reported as an electrolyte
Entry condition a percolating path for lithium must exist
Ionic conductivity is not a scored axis this season. Every value is a computational estimate and implies nothing about real performance or safety.
The board also carries seven electrolytes in actual use — LGPS, argyrodite, LLZO, LATP and others. They are scored but hold no rank. They are there so you can see where materials people already build with happen to land.
Compositions are private by default. Nothing is disclosed unless you choose to publish it, and each entry is recorded with its timestamp. If a third party asks to discuss a particular entry, we pass the request along — never the submitter's identity, unless they agree to it.
Season 1 runs 2026-08-21 to 11-30. A participation guide and a set of prompts are included.
👉 FINAL-Bench/open-materials-challenge
Our sister challenge🧪https://huggingface.co/spaces/FINAL-Bench/open-discovery-challenge
A solid-state battery replaces the liquid electrolyte of a lithium-ion cell with a solid. It does not catch fire, it lasts longer, and it can hold more. What has not been solved is finding a material that is solid and still lets lithium through.
Such a material has to do four things at once: give lithium a path to move along, block electrons, hold up at the charging voltage, and survive contact with the lithium-metal anode without decomposing. Plenty of materials manage three. Very few manage all four.
This challenge looks for candidates, together. You submit one composition — for example Li3YCl6. We score it computationally and place it on the board. There is no prize.
Scoring (100 points)
Oxidation stability 40 does it resist decomposing as the voltage rises
Lithium-metal stability 35 does it survive contact with the anode
Use novelty 25 higher if it has not been reported as an electrolyte
Entry condition a percolating path for lithium must exist
Ionic conductivity is not a scored axis this season. Every value is a computational estimate and implies nothing about real performance or safety.
The board also carries seven electrolytes in actual use — LGPS, argyrodite, LLZO, LATP and others. They are scored but hold no rank. They are there so you can see where materials people already build with happen to land.
Compositions are private by default. Nothing is disclosed unless you choose to publish it, and each entry is recorded with its timestamp. If a third party asks to discuss a particular entry, we pass the request along — never the submitter's identity, unless they agree to it.
Season 1 runs 2026-08-21 to 11-30. A participation guide and a set of prompts are included.
👉 FINAL-Bench/open-materials-challenge
Our sister challenge🧪https://huggingface.co/spaces/FINAL-Bench/open-discovery-challenge
liked a
model 11 days ago
liked 3
models 12 days ago
liked a
Space 12 days ago
updated a
Space 12 days ago
published a
Space 12 days ago
published a
model 13 days ago
reacted to Felladrin's post with 👀 13 days ago
Post
4142
I've open-sourced the trainer I've been using to build tiny language models from scratch, together with the 95M base model I trained with it.
The trainer runs on Deno (https://deno.com, cross-platform), trains on WebGPU, and it writes GGUF directly. No Python/PyTorch. The weights live in a GGUF file from the first step to the last, so every checkpoint is already something llama.cpp can load.
The model is Felladrin/Minueza-3-95M-Base: 94.7M parameters, 1.95B tokens seen, 8192 context.
And here’s the repository on GitHub: https://github.com/felladrin/gguf-trainer
Here on Hugging Face, I published the optimizer state next to the weights, so you can continue the pretraining instead of starting over.
Or start your own from nothing:
And the docs are written for coding agents, so you can point your agent of choice at the GitHub repo and have it drive the whole pipeline.
The trainer runs on Deno (https://deno.com, cross-platform), trains on WebGPU, and it writes GGUF directly. No Python/PyTorch. The weights live in a GGUF file from the first step to the last, so every checkpoint is already something llama.cpp can load.
The model is Felladrin/Minueza-3-95M-Base: 94.7M parameters, 1.95B tokens seen, 8192 context.
And here’s the repository on GitHub: https://github.com/felladrin/gguf-trainer
Here on Hugging Face, I published the optimizer state next to the weights, so you can continue the pretraining instead of starting over.
Or start your own from nothing:
deno run -A cli.ts demo trains a tiny one end to end in under a minute.And the docs are written for coding agents, so you can point your agent of choice at the GitHub repo and have it drive the whole pipeline.
- 36 replies