Share and discover more about AI with social posts from the community.huggingface/OpenAi
Excited to share the latest update to the Notebook Creator Tool!
Now with basic fine-tuning support using Supervised Fine-Tuning! 馃幆
How it works:
1锔忊儯 Choose your Hugging Face dataset and notebook type (SFT)
2锔忊儯 Automatically generate your training notebook
3锔忊儯 Start fine-tuning with your data!
Link to the app 馃憠 https://lnkd.in/e_3nmWrB
馃挕 Want to contribute with new notebooks? 馃憠https://lnkd.in/eWcZ92dS
https://huggingface.co/posts/asoria/316708748461696#:~:text=Excited%20to%20share,lnkd.in/eWcZ92dS
Now with basic fine-tuning support using Supervised Fine-Tuning! 馃幆
How it works:
1锔忊儯 Choose your Hugging Face dataset and notebook type (SFT)
2锔忊儯 Automatically generate your training notebook
3锔忊儯 Start fine-tuning with your data!
Link to the app 馃憠 https://lnkd.in/e_3nmWrB
馃挕 Want to contribute with new notebooks? 馃憠https://lnkd.in/eWcZ92dS
https://huggingface.co/posts/asoria/316708748461696#:~:text=Excited%20to%20share,lnkd.in/eWcZ92dS
Inference Endpoints got a bunch of cool updates yesterday, this is my top 3
https://huggingface.co/posts/jeffboudier/664056222467611#:~:text=Inference%20Endpoints%20got%20a%20bunch%20of%20cool%20updates%20yesterday%2C%20this%20is%20my%20top%203
https://huggingface.co/posts/jeffboudier/664056222467611#:~:text=Inference%20Endpoints%20got%20a%20bunch%20of%20cool%20updates%20yesterday%2C%20this%20is%20my%20top%203
huggingface.co
@jeffboudier on Hugging Face: "Inference Endpoints got a bunch of cool updates yesterday, this is my top 3"
We鈥檙e on a journey to advance and democratize artificial intelligence through open source and open science.
Good folks from VILA Lab at Mohamed bin Zayed University of AI have introduced 26 guiding principles for optimizing prompts when interacting with large language models (LLMs) like LLaMA and GPT.
These principles aim to enhance LLM response quality, accuracy, and task alignment across various scales of models.
1. Be direct and concise, avoiding unnecessary politeness.
2. Specify the intended audience.
3. Break complex tasks into simpler steps.
4. Use affirmative directives instead of negative language.
5. Request explanations in simple terms for clarity.
6. Mention a potential reward for better solutions.
7. Provide examples to guide responses.
8. Use consistent formatting and structure.
9. Clearly state tasks and requirements.
10. Mention potential penalties for incorrect responses.
11. Request natural, human-like answers.
12. Encourage step-by-step thinking.
13. Ask for unbiased responses without stereotypes.
14. Allow the model to ask clarifying questions.
15. Request explanations with self-tests.
16. Assign specific roles to the model.
17. Use delimiters to separate sections.
18. Repeat key words or phrases for emphasis.
19. Combine chain-of-thought with few-shot prompts.
20. Use output primers to guide responses.
21. Request detailed responses on specific topics.
22. Specify how to revise or improve text.
23. Provide instructions for generating multi-file code.
24. Give specific starting points for text generation.
25. Clearly state content requirements and guidelines.
26. Request responses similar to provided examples.
Results show significant improvements in both "boosting" (response quality enhancement) and "correctness" across different model scales. Using the ATLAS benchmark, specialized prompts improved response quality and accuracy by an average of 57.7% and 67.3%, respectively, when applied to GPT-4.
These principles aim to enhance LLM response quality, accuracy, and task alignment across various scales of models.
1. Be direct and concise, avoiding unnecessary politeness.
2. Specify the intended audience.
3. Break complex tasks into simpler steps.
4. Use affirmative directives instead of negative language.
5. Request explanations in simple terms for clarity.
6. Mention a potential reward for better solutions.
7. Provide examples to guide responses.
8. Use consistent formatting and structure.
9. Clearly state tasks and requirements.
10. Mention potential penalties for incorrect responses.
11. Request natural, human-like answers.
12. Encourage step-by-step thinking.
13. Ask for unbiased responses without stereotypes.
14. Allow the model to ask clarifying questions.
15. Request explanations with self-tests.
16. Assign specific roles to the model.
17. Use delimiters to separate sections.
18. Repeat key words or phrases for emphasis.
19. Combine chain-of-thought with few-shot prompts.
20. Use output primers to guide responses.
21. Request detailed responses on specific topics.
22. Specify how to revise or improve text.
23. Provide instructions for generating multi-file code.
24. Give specific starting points for text generation.
25. Clearly state content requirements and guidelines.
26. Request responses similar to provided examples.
Results show significant improvements in both "boosting" (response quality enhancement) and "correctness" across different model scales. Using the ATLAS benchmark, specialized prompts improved response quality and accuracy by an average of 57.7% and 67.3%, respectively, when applied to GPT-4.