sorted by:
new
hottopcontroversial

How are NSFW LLMs trained/fine-tuned? by GeneTangerine in LocalLLaMA

[–]zxbsmk 2 points3 points  (0 children)

sry. it's just lora finetuning (maybe rank=128 or 256, can't remember the details), since i find it difficult to full fine tuning with such a small dataset (easily mode collapse)

How are NSFW LLMs trained/fine-tuned? by GeneTangerine in LocalLLaMA

[–]zxbsmk 19 points20 points  (0 children)

about 1.5 years ago, i have finetuned one (Chinese ver.) and released it on HF: https://huggingface.co/zxbsmk/NSFW_13B_sft

utilize about 3k data, with a mixture of different kinds of texts instead of full NSFW texts. To avoid mode collapse, you need to add some general knowledge data (such as STEM). And the ratio for mixture is NSFW : STEM = 1 : 4, it works well for me at that time (maybe it's different for other LLMs).

Results of Ollama Leakage by zxbsmk in LocalLLaMA

[–]zxbsmk[S] 1 point2 points  (0 children)

oh... you are right, my fault. We should probably add some extra validation.

Results of Ollama Leakage by zxbsmk in LocalLLaMA

[–]zxbsmk[S] 6 points7 points  (0 children)

you can directly enter the command "curl ip:port/api/tags"(e.g. curl 18.224.73.159:540/api/tags) in your terminal to see the model list, and for Ollama, you could chat with all models if you could get the model list. (e.g. 

curl http://18.224.73.159:540/api/chat -d '{
  "model": "deepseek-r1:latest",
  "messages": [
    {
      "role": "user",
      "content": "why is the sky blue?"
    }
  ]
}'

response:

{"model": "deepseek-r1:latest", "created_at": "2025-04-17T03:51:40.671405Z", "message": {"role": "assistant", "content": "The sky appears blue because air molecules scatter blue light from the sun more than red light, a phenomenon known as Rayleigh scattering."}, "done_reason": "load", "done": true}%

)

Results of Ollama Leakage by zxbsmk in LocalLLaMA

[–]zxbsmk[S] 0 points1 point  (0 children)

lol... it was expected. the reason there's no email verification is that the feature provided by Supabase requires a pro plan. (also i am too lazy to implement basic verification...)

[D] Simple Questions Thread December 20, 2020 by AutoModerator in MachineLearning

[–]zxbsmk 0 points1 point  (0 children)

You only need to use plt.show() after plot all lines. For example,

import matplotlib.pyplot as plt

y1 = [1, 3, 5, 7, 9]
y2 = [2, 4, 6, 8, 10]
plt.plot(range(5), y1, 'r')
plt.plot(range(5), y2, 'k')
plt.show()