Mahdiyyah Hoosen
Mahdiyyah4
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reacted to
chansung's
post with β€οΈ
8 days ago
reacted to
giadap's
post with π₯
8 days ago
Post
2273
We've all become experts at clicking "I agree" without a second thought. In my latest blog post, I explore why these traditional consent models are increasingly problematic in the age of generative AI.
I found three fundamental challenges:
- Scope problem: how can you know what you're agreeing to when AI could use your data in different ways?
- Temporality problem: once an AI system learns from your data, good luck trying to make it "unlearn" it.
- Autonomy trap: the data you share today could create systems that pigeonhole you tomorrow.
Individual users shouldn't bear all the responsibility, while big tech holds all the cards. We need better approaches to level the playing field, from collective advocacy and stronger technological safeguards to establishing "data fiduciaries" with a legal duty to protect our digital interests.
Available here: https://huggingface.co/blog/giadap/beyond-consent
I found three fundamental challenges:
- Scope problem: how can you know what you're agreeing to when AI could use your data in different ways?
- Temporality problem: once an AI system learns from your data, good luck trying to make it "unlearn" it.
- Autonomy trap: the data you share today could create systems that pigeonhole you tomorrow.
Individual users shouldn't bear all the responsibility, while big tech holds all the cards. We need better approaches to level the playing field, from collective advocacy and stronger technological safeguards to establishing "data fiduciaries" with a legal duty to protect our digital interests.
Available here: https://huggingface.co/blog/giadap/beyond-consent
reacted to
openfree's
post with π₯
8 days ago
Post
6006
π DeepSeek V3-0324 + Real-time Research Power! π
Hello there! Today I'm excited to introduce an amazing tool based on the DeepSeek V3-0324 latest model. This isn't just another AI chatbotβit's a true "research assistant" capable of real-time information retrieval and analysis!
openfree/Deepseek-v3-0324-Research
π§ Key Strengths of DeepSeek V3-0324
DeepSeek V3-0324, provided by Fireworks AI, comes with these powerful advantages:
π― Superior Reasoning: Excellent ability to solve complex problems step-by-step
π Extensive Knowledge: Deep understanding across various topics from comprehensive training
𧩠Context Awareness: Maintains long conversation contexts for consistent responses
π Multilingual Support: Processes various languages effectively
π Added Real-time "Deep Research" Capability!
The most exciting feature of this project is the implementation of real-time search functionality similar to ChatGPT's Browse with Bing or Perplexity AI! π
How does it work?
π Query Analysis: Analyzes questions to automatically extract optimal search keywords
π Web Search: Utilizes advanced search technology to retrieve the latest information
π§ͺ Result Analysis: Intelligently analyzes search results and evaluates relevance
π‘ Comprehensive Response: Combines freshly retrieved information with AI's existing knowledge
Key Benefits:
β±οΈ Up-to-date Information: Always provides the latest data through real-time web searches
π Enhanced Reliability: Improves trustworthiness by citing information sources
π Overcoming Knowledge Limitations: Handles questions beyond the AI's training cutoff
π οΈ Research Efficiency: Processes everything from information retrieval to analysis in one go
π₯οΈ How to Use
It's simple! Just enable the "Deep Research" checkbox and ask your question. The AI will automatically search for and analyze relevant information to provide rich, informed answers.
Hello there! Today I'm excited to introduce an amazing tool based on the DeepSeek V3-0324 latest model. This isn't just another AI chatbotβit's a true "research assistant" capable of real-time information retrieval and analysis!
openfree/Deepseek-v3-0324-Research
π§ Key Strengths of DeepSeek V3-0324
DeepSeek V3-0324, provided by Fireworks AI, comes with these powerful advantages:
π― Superior Reasoning: Excellent ability to solve complex problems step-by-step
π Extensive Knowledge: Deep understanding across various topics from comprehensive training
𧩠Context Awareness: Maintains long conversation contexts for consistent responses
π Multilingual Support: Processes various languages effectively
π Added Real-time "Deep Research" Capability!
The most exciting feature of this project is the implementation of real-time search functionality similar to ChatGPT's Browse with Bing or Perplexity AI! π
How does it work?
π Query Analysis: Analyzes questions to automatically extract optimal search keywords
π Web Search: Utilizes advanced search technology to retrieve the latest information
π§ͺ Result Analysis: Intelligently analyzes search results and evaluates relevance
π‘ Comprehensive Response: Combines freshly retrieved information with AI's existing knowledge
Key Benefits:
β±οΈ Up-to-date Information: Always provides the latest data through real-time web searches
π Enhanced Reliability: Improves trustworthiness by citing information sources
π Overcoming Knowledge Limitations: Handles questions beyond the AI's training cutoff
π οΈ Research Efficiency: Processes everything from information retrieval to analysis in one go
π₯οΈ How to Use
It's simple! Just enable the "Deep Research" checkbox and ask your question. The AI will automatically search for and analyze relevant information to provide rich, informed answers.
reacted to
eaddario's
post with π
12 days ago
Post
2723
Squeezing Tensor Bits: the quest for smaller LLMs
An area of personal interest is finding ways to optimize the inference performance of LLMs when deployed in resource-constrained environments like commodity hardware, desktops, laptops, mobiles, edge devices, etc.
The method that I'm using to produce these experimental versions, for example eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF is explained in https://medium.com/@eaddario/squeezing-tensor-bits-the-quest-for-smaller-llms-86b23bd052ca
At a high level it involves using a custom version of the llama-quantize tool to selectively quantize different tensors at different levels. On average a 10% or more reduction with little loss of quality is possible.
Thereβre two PRs to merge these changes back into the core project but until then, the modified version will be available on GitHub https://github.com/EAddario/llama.cpp/tree/quantize
Would love to hear if you can achieve smaller sizes at higher quality!
An area of personal interest is finding ways to optimize the inference performance of LLMs when deployed in resource-constrained environments like commodity hardware, desktops, laptops, mobiles, edge devices, etc.
The method that I'm using to produce these experimental versions, for example eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF is explained in https://medium.com/@eaddario/squeezing-tensor-bits-the-quest-for-smaller-llms-86b23bd052ca
At a high level it involves using a custom version of the llama-quantize tool to selectively quantize different tensors at different levels. On average a 10% or more reduction with little loss of quality is possible.
Thereβre two PRs to merge these changes back into the core project but until then, the modified version will be available on GitHub https://github.com/EAddario/llama.cpp/tree/quantize
Would love to hear if you can achieve smaller sizes at higher quality!