Koen Botermans

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liked a model 19 days ago

ssmits/ModernBERT-base-dutch-test

liked a model 19 days ago

ssmits/Qwen2.5-7B-embed-base

reacted to mitkox's post with 🚀 19 days ago

I run 20 AI coding agents locally on my desktop workstation at 400+ tokens/sec with MiniMax-M2. It’s a Sonnet drop-in replacement in my Cursor, Claude Code, Droid, Kilo and Cline peak at 11k tok/sec input and 433 tok/s output, can generate 1B+ tok/m.All with 196k context window. I'm running it for 6 days now with this config. Today max performance was stable at 490.2 tokens/sec across 48 concurrent clients and MiniMax M2. Z8 Fury G5, Xeon 3455, 4xA6K. Aibrix 0.5.0, vLLM 0.11.2,

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liked 2 models 19 days ago

ssmits/ModernBERT-base-dutch-test

0.1B • Updated Dec 22, 2024 • 11 • 2

ssmits/Qwen2.5-7B-embed-base

Text Classification • 7B • Updated Nov 12 • 87 • 2

reacted to mitkox's post with 🚀🔥 19 days ago

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I run 20 AI coding agents locally on my desktop workstation at 400+ tokens/sec with MiniMax-M2. It’s a Sonnet drop-in replacement in my Cursor, Claude Code, Droid, Kilo and Cline peak at 11k tok/sec input and 433 tok/s output, can generate 1B+ tok/m.All with 196k context window. I'm running it for 6 days now with this config.

Today max performance was stable at 490.2 tokens/sec across 48 concurrent clients and MiniMax M2.

Z8 Fury G5, Xeon 3455, 4xA6K. Aibrix 0.5.0, vLLM 0.11.2,

5 replies

upvoted an article 19 days ago

Article

Introducing MTEB v2: Evaluation of embedding and retrieval systems for more than just text

Oct 20

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replied to sherryxychen's post 29 days ago

Hi @sherryxychen ,

Thanks a lot for the writing this down, what you wrote down really helped!

But I have a question about "Built proper eval pipeline with progress scoring" What do you mean exactly with this? I assume it is from this file: https://github.com/sherrychen1120/so101_bench/blob/main/src/so101_bench/task_progress_labeler.py

But I can't really figure out what happens in that file.

Thanks again!

reacted to sherryxychen's post with 🤗 29 days ago

Post

406

**Training ACT on SO-101: From Woodpecker to 90% Success (All the Mistakes Included)**

I spent 3 weeks training Action Chunking Transformer on SO-101 for pick-and-place. Spoiler: the first attempt trained a woodpecker that just pecked the table. 🐦

**What's Different About This Post:**
Most ACT tutorials show the success. I documented every failure, hardware issue, and debugging step. If you're new to SO-101/LeRobot/ACT, hopefully my mistakes save you time.

Try 1: The Woodpecker
- Followed the LeRobot tutorial, collected 50 episodes
- Beautiful loss curves ✅
- Robot learned to peck at table ❌
- Rookie mistakes: moving cameras, arm calibration mismatch, limited data diversity, looking at follower arm during teleop (it's cheating!)

Try 2: Engineering Upgrades
- Fixed hardware setup (tape + markers everywhere)
- USB udev rules for camera stability
- Formal task definition with stratified sampling
- Built proper eval pipeline with progress scoring
- Motor breakdown mid-collection (broke the gripper with excessive force 💀)
- Results: 60% in-distribution success, 10% OOD (better, but not great)

Try 3: More & Better Data
sherryxychen/2025-09-07_act
- 125 episodes with rotation variations ( sherryxychen/2025-09-01_pick-and-place-block)
- Better workspace coverage
- Improved grasping technique
- Results: 90% in-distribution success, 75% OOD! 🎉

Key Learnings:
- Consistent hardware setup is everything
- Don't look at the follower arm during teleop
- Data diversity is key for generalization
- Debug infrastructure matters
- Real robots break in mysterious ways (buy spare motors!)

Full write-up (with more videos!): https://huggingface.co/blog/sherryxychen/train-act-on-so-101

Code: https://github.com/sherrychen1120/so101_bench

Happy to answer any questions! 🤗

#imitation-learning #lerobot #act #so-101