Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2
Image-Text-to-Text • 10B • Updated • 23.1k • 111
Pyro
pyrod3v
AI & ML interests
None yet
Recent Activity
liked a model 4 days ago
Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2 liked a model 8 months ago
kyutai/tts-1.6b-en_fr liked a model 8 months ago
openai/gpt-oss-20bOrganizations
None yet
liked a
model 4 days ago
liked 2
models 8 months ago
liked 3
datasets 8 months ago
reactedto jasoncorkill's post with 🔥😎👀 8 months ago
Post
3284
"Why did the bee get married?"
"Because he found his honey!"
This was the "funniest" joke out of 10'000 jokes we generated with LLMs. With 68% of respondents rating it as "funny".
Original jokes are particularly hard for LLMs, as jokes are very nuanced and a lot of context is needed to understand if something is "funny". Something that can only reliably be measured using humans.
LLMs are not equally good at generating jokes in every language. Generated English jokes turned out to be way funnier than the Japanese ones. 46% of English-speaking voters on average found the generated joke funny. The same statistic for other languages:
Vietnamese: 44%
Portuguese: 40%
Arabic: 37%
Japanese: 28%
There is not much variance in generation quality among models for any fixed language. But still Claude Sonnet 4 slightly outperforms others in Vietnamese, Arabic and Japanese and Gemini 2.5 Flash in Portuguese and English
We have release the 1 Million (!) native speaker ratings and the 10'000 jokes as a dataset for anyone to use:
Rapidata/multilingual-llm-jokes-4o-claude-gemini
"Because he found his honey!"
This was the "funniest" joke out of 10'000 jokes we generated with LLMs. With 68% of respondents rating it as "funny".
Original jokes are particularly hard for LLMs, as jokes are very nuanced and a lot of context is needed to understand if something is "funny". Something that can only reliably be measured using humans.
LLMs are not equally good at generating jokes in every language. Generated English jokes turned out to be way funnier than the Japanese ones. 46% of English-speaking voters on average found the generated joke funny. The same statistic for other languages:
Vietnamese: 44%
Portuguese: 40%
Arabic: 37%
Japanese: 28%
There is not much variance in generation quality among models for any fixed language. But still Claude Sonnet 4 slightly outperforms others in Vietnamese, Arabic and Japanese and Gemini 2.5 Flash in Portuguese and English
We have release the 1 Million (!) native speaker ratings and the 10'000 jokes as a dataset for anyone to use:
Rapidata/multilingual-llm-jokes-4o-claude-gemini
- 7 replies
liked a
dataset 8 months ago
liked 5
models 9 months ago
HuggingFaceTB/SmolLM3-3B
Text Generation • 3B • Updated • 1.09M • 916
zai-org/GLM-4.1V-9B-Thinking
Image-Text-to-Text • 10B • Updated • 421k • 773
tencent/Hunyuan-A13B-Instruct
Text Generation • Updated • 21.8k • 616
Menlo/Jan-nano
Text Generation • 4B • Updated • 4.91k • • 502
openbmb/MiniCPM4-0.5B
Text Generation • 0.4B • Updated • 19.2k • 77
liked 2
models 10 months ago
reactedto merve's post with 🔥 10 months ago
Post
3051
Qwen2.5-Omni is soooo good that people build multimodal reasoning models off of it 🥹
> KE-Team/Ke-Omni-R-3B is open-source audio reasoning model sota on average of benchmarks, based on Qwen/Qwen2.5-Omni-3B 🗣️
> Haoz0206/Omni-R1 is a video reasoning model with pixel level grounding (see below) and it's super competitive ⏯️ based on Qwen/Qwen2.5-Omni-7B
> KE-Team/Ke-Omni-R-3B is open-source audio reasoning model sota on average of benchmarks, based on Qwen/Qwen2.5-Omni-3B 🗣️
> Haoz0206/Omni-R1 is a video reasoning model with pixel level grounding (see below) and it's super competitive ⏯️ based on Qwen/Qwen2.5-Omni-7B
liked 2
models 10 months ago
