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WebApp1K Models Leaderboard
🥇
Display pass@k metrics for models and scenarios
Yi Cui
onekq
AI & ML interests
Benchmark, Code Generation Model
Recent Activity
updated
a Space
1 day ago
onekq-ai/WebApp1K-models-leaderboard
posted
an
update
2 days ago
QwQ-32B is amazing!
It ranks below o1-preview, but beats DeepSeek v3 and all Gemini models.
https://huggingface.co./spaces/onekq-ai/WebApp1K-models-leaderboard
Now we have such a powerful model that can fit into a single GPU, can someone finetune a web app model to push SOTA of my leaderboard? 🤗
posted
an
update
3 days ago
From my own experience these are the pain points for reasoning model adoption.
(1) expensive and even worse, slow, due to excessive token output. You need to 10x your max output length to avoid clipping the thinking process.
(2) you have to filter thinking tokens to retrieve the final output. For mature workflows, this means broad or deep refactoring.
1p vendors (open-source and proprietary) ease these pain points by manipulating their own models. But the problems are exposed when the reasoning model is hosted by 3p MaaS providers.
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onekq's activity
updated
a
Space
1 day ago
posted
an
update
2 days ago
Post
2464
QwQ-32B is amazing!
It ranks below o1-preview, but beats DeepSeek v3 and all Gemini models.
onekq-ai/WebApp1K-models-leaderboard
Now we have such a powerful model that can fit into a single GPU, can someone finetune a web app model to push SOTA of my leaderboard? 🤗
It ranks below o1-preview, but beats DeepSeek v3 and all Gemini models.
onekq-ai/WebApp1K-models-leaderboard
Now we have such a powerful model that can fit into a single GPU, can someone finetune a web app model to push SOTA of my leaderboard? 🤗
posted
an
update
3 days ago
Post
492
From my own experience these are the pain points for reasoning model adoption.
(1) expensive and even worse, slow, due to excessive token output. You need to 10x your max output length to avoid clipping the thinking process.
(2) you have to filter thinking tokens to retrieve the final output. For mature workflows, this means broad or deep refactoring.
1p vendors (open-source and proprietary) ease these pain points by manipulating their own models. But the problems are exposed when the reasoning model is hosted by 3p MaaS providers.
(1) expensive and even worse, slow, due to excessive token output. You need to 10x your max output length to avoid clipping the thinking process.
(2) you have to filter thinking tokens to retrieve the final output. For mature workflows, this means broad or deep refactoring.
1p vendors (open-source and proprietary) ease these pain points by manipulating their own models. But the problems are exposed when the reasoning model is hosted by 3p MaaS providers.
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