Pruna 0.3.2: More OSS Algos, More Ways to Optimize
โข 5
AI & ML interests
Efficient machine learning for any model and hardware: pruning, quantization, compilation, and more.
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46
Hey! We've released PrunaVAED, a drop-in replacement decoder for LTX-2.3
- ~1.7-2.1x faster decoder & ~50% lower peak VRAM
- Near-original visual quality & no changes to the latent encoding
Check it here: PrunaAI/PrunaVAED
- ~1.7-2.1x faster decoder & ~50% lower peak VRAM
- Near-original visual quality & no changes to the latent encoding
Check it here: PrunaAI/PrunaVAED
mehdiPrunaAIย
published a
model about 12 hours ago
mehdiPrunaAIย
updated a
model about 17 hours ago
Update README.md
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#1 opened 1 day ago
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sdiazlor
Update README.md
๐ 1
#1 opened 1 day ago
by sdiazlor
davidberenstein1957ย
updated a
dataset 6 days ago
davidberenstein1957ย
published a
dataset 12 days ago
Post
177
As First Prune, the one-year Pruna OSS anniversary, is halfway.
Weโre sharing a recap blog post about our OSS journey โ how we started, what weโve built so far, and whatโs next.
Read it here: https://dev.to/pruna-ai/first-prune-celebrate-one-year-of-pruna-oss-50gp
Weโre sharing a recap blog post about our OSS journey โ how we started, what weโve built so far, and whatโs next.
Read it here: https://dev.to/pruna-ai/first-prune-celebrate-one-year-of-pruna-oss-50gp
Post
114
Pruna OSS is turning 1! To mark this milestone, we're launching the First Prune initiative.
What's First Prune:
If you contribute to open issues at our GitHub repo, you earn Pruna Inference API credits.
How you can participate:
โข Pick an open issue labelled "first-prune" and assign it to you
โข Submit your PR and mark it ready for review by April 30
โข Find out more in the PR template when you open a PR
Each merged PR scores 30 credits.
Letโs build something great together! Find your issue: https://github.com/PrunaAI/pruna/issues
What's First Prune:
If you contribute to open issues at our GitHub repo, you earn Pruna Inference API credits.
How you can participate:
โข Pick an open issue labelled "first-prune" and assign it to you
โข Submit your PR and mark it ready for review by April 30
โข Find out more in the PR template when you open a PR
Each merged PR scores 30 credits.
Letโs build something great together! Find your issue: https://github.com/PrunaAI/pruna/issues
Post
2614
More OSS than ever with the latest pruna 0.3.2 release. It extends existing algorithm families, such as compilers, kernels, and pruners, and adds new ones, including decoders, distillers, enhancers, and recoverers. But it's not only a collection of algorithms; instead, you can easily combine them to get the biggest efficiency win.
Read the full blog here: https://huggingface.co/blog/PrunaAI/pruna-0-3-2-open-source-optimization-algorithms
Read the full blog here: https://huggingface.co/blog/PrunaAI/pruna-0-3-2-open-source-optimization-algorithms
davidberenstein1957ย
posted an update 7 months ago
Post
2848
๐จ Phare LLM benchmark V2: Reasoning models don't guarantee better security
Read the full blog here: https://huggingface.co/blog/davidberenstein1957/phare-llm-benchmark-v2
Read the full blog here: https://huggingface.co/blog/davidberenstein1957/phare-llm-benchmark-v2
davidberenstein1957ย
posted an update about 1 year ago
Post
634
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
davidberenstein1957ย
posted an update about 1 year ago
Post
423
๐จ 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
- 1 reply
davidberenstein1957ย
posted an update about 1 year ago
Post
1780
I created a collection of FLUX.1 models but 4x faster PrunaAI/flux1-but-4x-faster-66c0b7340836dd7a55e9c0ea
davidberenstein1957ย
posted an update about 1 year ago
Post
1107
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
Post
3217
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.
๐ It is designed for a **large audience** including beginners, decision-makers, engineers, and researchers.
๐ 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! ๐
https://github.com/PrunaAI/awesome-ai-efficiency
๐ It is designed for a **large audience** including beginners, decision-makers, engineers, and researchers.
๐ 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! ๐
https://github.com/PrunaAI/awesome-ai-efficiency
- 2 replies
davidberenstein1957ย
posted an update over 1 year ago
Post
2290
๐ฅ Announcing FLUX-Juiced: The Fastest Image Generation Endpoint (2.6x faster)!
Optimisations are widely applied and can reduce inference time, but their impact on quality often remains unclear, so we decided to challenge the status quo and create our own optimised version of FLUX.1[dev] called FLUX-juiced.
Blog: https://huggingface.co/blog/PrunaAI/flux-fastest-image-generation-endpoint
Optimisations are widely applied and can reduce inference time, but their impact on quality often remains unclear, so we decided to challenge the status quo and create our own optimised version of FLUX.1[dev] called FLUX-juiced.
Blog: https://huggingface.co/blog/PrunaAI/flux-fastest-image-generation-endpoint
davidberenstein1957ย
posted an update over 1 year ago
Post
1752
๐งโ๐ซ I wrote a brief blogpost to give An Introduction to AI Model Optimization Techniques!
URL: https://huggingface.co/blog/PrunaAI/introduction-to-ai-model-optimization-techniques
URL: https://huggingface.co/blog/PrunaAI/introduction-to-ai-model-optimization-techniques
davidberenstein1957ย
posted an update over 1 year ago
Post
1415
RealHarm: A Collection of Real-World Language Model Application Failure
I'm David from Giskard, and we work on securing your Agents.
Today, we are launching RealHarm: a dataset of real-world problematic interactions with AI agents, drawn from publicly reported incidents.
Check out the dataset and paper: https://realharm.giskard.ai/
I'm David from Giskard, and we work on securing your Agents.
Today, we are launching RealHarm: a dataset of real-world problematic interactions with AI agents, drawn from publicly reported incidents.
Check out the dataset and paper: https://realharm.giskard.ai/
davidberenstein1957ย
posted an update over 1 year ago
Post
2118
๐จ New Bonus Unit: Tracing & Evaluating Your Agent! ๐จ
Learn how to transform your agent from a simple demo into a robust, reliable product ready for real users.
UNIT: https://huggingface.co/learn/agents-course/bonus-unit2/introduction
In this unit, you'll learn:
- Offline Evaluation โ Benchmark and iterate your agent using datasets.
- Online Evaluation โ Continuously track key metrics such as latency, costs, and user feedback.
Happy testing and improving!
Thanks Langfuse team!
Learn how to transform your agent from a simple demo into a robust, reliable product ready for real users.
UNIT: https://huggingface.co/learn/agents-course/bonus-unit2/introduction
In this unit, you'll learn:
- Offline Evaluation โ Benchmark and iterate your agent using datasets.
- Online Evaluation โ Continuously track key metrics such as latency, costs, and user feedback.
Happy testing and improving!
Thanks Langfuse team!