Asimov 1
Collection
Asimov 1 locomotion models and related resources. • 1 item • Updated
Jan Team
jan-ai
AI & ML interests
Also at https://huggingface.co/janhq
Recent Activity
new activity about 22 hours ago
Menlo/asimov1-locomotion-0818:Metadata for robotics policy model for Asimov 🔥 updated a collection 2 days ago
Asimov 1 updated a model 2 days ago
Menlo/asimov1-locomotion-0818Organizations
Metadata for robotics policy model for Asimov 🔥
#1 opened about 22 hours ago
by 
julien-c
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model 2 days ago
published 2
models 6 months ago
updated 2
models 6 months ago
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updated 3
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published 2
models 7 months ago
reacted to alandao's post with 🔥 7 months ago
Post
1545
Don’t give up 🔥
Do you know what I was planning to do this time last week?
I was preparing to write a report declaring that Jan Nano was a failed project because the benchmark results didn’t meet expectations.
But I thought — it can’t be. When loading the model into the app, the performance clearly felt better. So why were the benchmark results worse?
That’s when I reviewed the entire benchmark codebase and realized something fundamental: agentic or workflow-based approaches introduce a huge gap and variation when benchmarking. Jan-nano was trained with an agentic setup — it simply can’t be benchmarked using a rigid workflow-based method.
I made the necessary changes, and the model ended up performing even better than before the issues arose. Turns out the previous benchmarking method conflicted with the way the model was trained.
What if I had given up? That would’ve meant 1.5 months of training and a huge amount of company resources wasted.
But now, this is officially the most successful and biggest release for the whole team — all thanks to Jan-nano.
Menlo/Jan-nano
Do you know what I was planning to do this time last week?
I was preparing to write a report declaring that Jan Nano was a failed project because the benchmark results didn’t meet expectations.
But I thought — it can’t be. When loading the model into the app, the performance clearly felt better. So why were the benchmark results worse?
That’s when I reviewed the entire benchmark codebase and realized something fundamental: agentic or workflow-based approaches introduce a huge gap and variation when benchmarking. Jan-nano was trained with an agentic setup — it simply can’t be benchmarked using a rigid workflow-based method.
I made the necessary changes, and the model ended up performing even better than before the issues arose. Turns out the previous benchmarking method conflicted with the way the model was trained.
What if I had given up? That would’ve meant 1.5 months of training and a huge amount of company resources wasted.
But now, this is officially the most successful and biggest release for the whole team — all thanks to Jan-nano.
Menlo/Jan-nano
updated 2
models 8 months ago
Update README.md
#3 opened 8 months ago
by 
jan-ai
liked 2
models 8 months ago