NePe/Qwen3-30B-A3B-GPTQ
Text Generation
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Updated
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Peter Kis
NePe
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updated
a model
about 15 hours ago
NePe/Qwen3-30B-A3B-GPTQ
published
a model
about 15 hours ago
NePe/Qwen3-30B-A3B-GPTQ
new activity
1 day ago
JunHowie/Qwen3-30B-A3B-GPTQ-Int4:Slow GPTQ inference
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NePe's activity
updated
a
model
about 15 hours ago
published
a
model
about 15 hours ago
Slow GPTQ inference
4
#2 opened 2 days ago
by
NePe
Slow GPTQ inference
9
#1 opened 2 days ago
by
NePe
liked
2
models
2 days ago
PEFT finetuning support
#14 opened 9 days ago
by
NePe
liked
a
model
15 days ago
liked
a
model
about 1 month ago
Can't determine properly which is greater between 9.9 and 9.11
2
10
#38 opened about 1 month ago
by
sniffski
liked
a
model
about 1 month ago
liked
a
model
7 months ago
upvoted
2
papers
9 months ago
with load_in_4bit it just generates <pad> tokens
2
#16 opened 10 months ago
by
NePe
it just keeps generating <pad> tokens
3
#1 opened 10 months ago
by
NePe
liked
a
model
10 months ago
reacted to
santiviquez's
post with 🔥
11 months ago
Post
1568
I ran 580 experiments (yes, 580 🤯) to check if we can quantify data drift's impact on model performance using only drift metrics.
For these experiments, I built a technique that relies on drift signals to estimate model performance. I compared its results against the current SoTA performance estimation methods and checked which technique performs best.
The plot below summarizes the general results. It measures the quality of performance estimation versus the absolute performance change. (The lower, the better).
Full experiment: https://www.nannyml.com/blog/data-drift-estimate-model-performance
In it, I describe the setup, datasets, models, benchmarking methods, and the code used in the project.
For these experiments, I built a technique that relies on drift signals to estimate model performance. I compared its results against the current SoTA performance estimation methods and checked which technique performs best.
The plot below summarizes the general results. It measures the quality of performance estimation versus the absolute performance change. (The lower, the better).
Full experiment: https://www.nannyml.com/blog/data-drift-estimate-model-performance
In it, I describe the setup, datasets, models, benchmarking methods, and the code used in the project.
liked
a
model
11 months ago
reacted to
andrewrreed's
post with ❤️
12 months ago
Post
2623
🔬 Open LLM Progress Tracker 🔬
Inspired by the awesome work from @mlabonne , I created a Space to monitor the narrowing gap between open and proprietary LLMs as scored by the LMSYS Chatbot Arena ELO ratings 🤗
The goal is to have a continuously updated place to easily visualize these rapidly evolving industry trends 🚀
🔗 Open LLM Progress Tracker: andrewrreed/closed-vs-open-arena-elo
🔗 Source of Inspiration: https://www.linkedin.com/posts/maxime-labonne_arena-elo-graph-updated-with-new-models-activity-7187062633735368705-u2jB/
Inspired by the awesome work from @mlabonne , I created a Space to monitor the narrowing gap between open and proprietary LLMs as scored by the LMSYS Chatbot Arena ELO ratings 🤗
The goal is to have a continuously updated place to easily visualize these rapidly evolving industry trends 🚀
🔗 Open LLM Progress Tracker: andrewrreed/closed-vs-open-arena-elo
🔗 Source of Inspiration: https://www.linkedin.com/posts/maxime-labonne_arena-elo-graph-updated-with-new-models-activity-7187062633735368705-u2jB/
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