Jesus@lemmy.world to Political Memes@lemmy.world · 3 days agoWhat could possibly go wronglemmy.worldimagemessage-square78fedilinkarrow-up1560arrow-down149
arrow-up1511arrow-down1imageWhat could possibly go wronglemmy.worldJesus@lemmy.world to Political Memes@lemmy.world · 3 days agomessage-square78fedilink
minus-squareEven_Adder@lemmy.dbzer0.comlinkfedilinkEnglisharrow-up56arrow-down3·3 days agoThe answer I got out of DeepSeek-R1-Distill-Llama-8B-abliterate.i1-Q4_K_S
minus-squaretaiyang@lemmy.worldlinkfedilinkarrow-up29arrow-down1·3 days agoSo a real answer, basically. Too bad your average person isn’t going to bother with that. Still nice it’s open source.
minus-squarefelixwhynot@lemmy.worldlinkfedilinkarrow-up11·2 days agoSeems like the model you mentioned is more like a fine tuned Llama? Specifically, these are fine-tuned versions of Qwen and Llama, on a dataset of 800k samples generated by DeepSeek R1. https://github.com/Emericen/deepseek-r1-distilled
minus-squareEven_Adder@lemmy.dbzer0.comlinkfedilinkEnglisharrow-up8·edit-22 days agoYeah, it’s distilled from deepseek and abliterated. The non-abliterated ones give you the same responses as Deepseek R1.
The answer I got out of DeepSeek-R1-Distill-Llama-8B-abliterate.i1-Q4_K_S
So a real answer, basically. Too bad your average person isn’t going to bother with that. Still nice it’s open source.
Seems like the model you mentioned is more like a fine tuned Llama?
https://github.com/Emericen/deepseek-r1-distilled
Yeah, it’s distilled from deepseek and abliterated. The non-abliterated ones give you the same responses as Deepseek R1.
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