Measuring What Matters: Objective Metrics for Image Generation Assessment
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Efficient machine learning for any model and hardware: pruning, quantization, compilation, and more.
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johnrachwanprunaΒ
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johnrachwanprunaΒ
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sharpenbΒ
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sharpenbΒ
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28 days ago
sharpenbΒ
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28 days ago
sharpenbΒ
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28 days ago
johnrachwanprunaΒ
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davidberenstein1957Β
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johnrachwanprunaΒ
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sharpenbΒ
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Space
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davidberenstein1957Β
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Space
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davidberenstein1957Β
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davidberenstein1957Β
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2 months ago
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418
Announcing RealPerformance, a dataset of functional issues of language models that mirrors failure patterns identified through rigorous testing in real LLM agents
https://huggingface.co/blog/davidberenstein1957/realperformance-llm-business-compliance
https://huggingface.co/blog/davidberenstein1957/realperformance-llm-business-compliance
davidberenstein1957Β
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3 months ago
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365
π¨ LLMs recognise bias but also reproduce harmful stereotypes: an analysis of bias in leading LLMs
I've written a new entry in our series on the Giskard, BPIFrance and Google Deepmind Phare benchmark(phare.giskard.ai).
This time it covers bias: https://huggingface.co/blog/davidberenstein1957/llms-recognise-bias-but-also-produce-stereotypes
Previous entry on hallucinations: https://huggingface.co/blog/davidberenstein1957/phare-analysis-of-hallucination-in-leading-llms
I've written a new entry in our series on the Giskard, BPIFrance and Google Deepmind Phare benchmark(phare.giskard.ai).
This time it covers bias: https://huggingface.co/blog/davidberenstein1957/llms-recognise-bias-but-also-produce-stereotypes
Previous entry on hallucinations: https://huggingface.co/blog/davidberenstein1957/phare-analysis-of-hallucination-in-leading-llms
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davidberenstein1957Β
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4 months ago
Post
1738
I created a collection of FLUX.1 models but 4x faster
PrunaAI/flux1-but-4x-faster-66c0b7340836dd7a55e9c0ea
davidberenstein1957Β
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5 months ago
Post
1070
Good answers are not necessarily factual answers: an analysis of hallucination in leading LLMs
https://huggingface.co/blog/davidberenstein1957/phare-analysis-of-hallucination-in-leading-llms
https://huggingface.co/blog/davidberenstein1957/phare-analysis-of-hallucination-in-leading-llms
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3180
How to learn about efficient AI? - Happy to announce the Awesome AI Efficiency repo that gathers a curated list of 100+ materials to understand the challenges and solutions in making AI faster, smaller, cheaper, greener.
π It is designed for a **large audience** including beginners, decision-makers, engineers, and researchers.
π It contains **diverse materials** with newspaper articles, blogs, tools, tech reports, research papers, books, and lectures.
This is an ongoing project. Do not hesitate to share your feedback/suggestions and star the repo! π
https://github.com/PrunaAI/awesome-ai-efficiency
π It is designed for a **large audience** including beginners, decision-makers, engineers, and researchers.
π It contains **diverse materials** with newspaper articles, blogs, tools, tech reports, research papers, books, and lectures.
This is an ongoing project. Do not hesitate to share your feedback/suggestions and star the repo! π
https://github.com/PrunaAI/awesome-ai-efficiency
- 2 replies