π₯Β Announcing FLUX-Juiced: The Fastest Image Generation Endpoint (2.6 times faster)!
β’
9
Pruna AI
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AI & ML interests
Efficient machine learning for any model and hardware: pruning, quantization, compilation, and more.
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davidberenstein1957Β
posted
an
update
5 days ago
Post
2093
π₯ 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
davidberenstein1957Β
updated
a
Space
6 days ago
davidberenstein1957Β
posted
an
update
10 days ago
Post
1680
π§βπ« 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
davidberenstein1957Β
posted
an
update
12 days ago
Post
1373
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/
davidberenstein1957Β
updated
a
dataset
13 days ago
davidberenstein1957Β
published
a
dataset
16 days ago
Upload folder using huggingface_hub
2
#5 opened 25 days ago
by
sharpenb
Upload folder using huggingface_hub
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#3 opened 25 days ago
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sharpenb
Upload folder using huggingface_hub
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#2 opened 25 days ago
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sharpenb
Upload folder using huggingface_hub
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#6 opened 25 days ago
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sharpenb
Upload folder using huggingface_hub
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#3 opened 25 days ago
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sharpenb
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#9 opened 25 days ago
by
sharpenb
davidberenstein1957Β
posted
an
update
about 1 month ago
Post
2086
π¨ New Bonus Unit: Tracing & Evaluating Your Agent! π¨
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 β Benchmark and iterate your agent using datasets.
- Online Evaluation β 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 β Benchmark and iterate your agent using datasets.
- Online Evaluation β Continuously track key metrics such as latency, costs, and user feedback.
Happy testing and improving!
Thanks Langfuse team!
Post
3093
We open-sourced the
- Github repo: https://github.com/PrunaAI/pruna
- Documentation: https://docs.pruna.ai/en/stable/index.html
With open-sourcing, people can now inspect and contribute to the open code. Beyond the code, we provide detailed readme, tutorials, benchmarks, and documentation to make transparent compression, evaluation, and saving/loading/serving of AI models.
Happy to share it with you and always interested in collecting your feedback :)
pruna package that can be easily installed with pip install pruna :) It allows to easily ccompress and evaluate AI models including transformers and diffusers.- Github repo: https://github.com/PrunaAI/pruna
- Documentation: https://docs.pruna.ai/en/stable/index.html
With open-sourcing, people can now inspect and contribute to the open code. Beyond the code, we provide detailed readme, tutorials, benchmarks, and documentation to make transparent compression, evaluation, and saving/loading/serving of AI models.
Happy to share it with you and always interested in collecting your feedback :)
- 2 replies
davidberenstein1957Β
posted
an
update
about 2 months ago
Post
2413
π₯ Text2SQL, explore and share any data analysis!
π€ Hugging Face - Dataset Studio is an amazing new feature.
π Start yourself: fka/awesome-chatgpt-prompts
πΊ YouTube: https://youtu.be/5LUZq7MHolA?feature=shared
π€ Hugging Face - Dataset Studio is an amazing new feature.
π Start yourself: fka/awesome-chatgpt-prompts
πΊ YouTube: https://youtu.be/5LUZq7MHolA?feature=shared
davidberenstein1957Β
posted
an
update
about 2 months ago
Post
4244
π₯ Epic Agent Framework Showdown! Available today!
π΅ In the blue corner, the versatile challenger with a proven track record of knowledge retrieval: LlamaIndex!
π In the red corner, the defender, weighing in with lightweight efficiency: Hugging Face smolagents!
π URL:
agents-course
We just published the LlamaIndex unit for the agents course, and it is set to offer a great contrast between the smolagents unit by looking at
- What makes llama-index stand-out
- How the LlamaHub is used for integrations
- Creating QueryEngine components
- Using agents and tools
- Agentic and multi-agent workflows
The team has been working flat-out on this for a few weeks. Supported by Logan Markewich and Laurie Voss over at LlamaIndex.
Who won? You decide!
π΅ In the blue corner, the versatile challenger with a proven track record of knowledge retrieval: LlamaIndex!
π In the red corner, the defender, weighing in with lightweight efficiency: Hugging Face smolagents!
π URL:
agents-course
We just published the LlamaIndex unit for the agents course, and it is set to offer a great contrast between the smolagents unit by looking at
- What makes llama-index stand-out
- How the LlamaHub is used for integrations
- Creating QueryEngine components
- Using agents and tools
- Agentic and multi-agent workflows
The team has been working flat-out on this for a few weeks. Supported by Logan Markewich and Laurie Voss over at LlamaIndex.
Who won? You decide!
davidberenstein1957Β
posted
an
update
about 2 months ago
Post
3040
π«Έ New release to push vector search to the Hub with vicinity and work with any serialisable objects.
π§βπ« KNN, HNSW, USEARCH, ANNOY, PYNNDESCENT, FAISS, and VOYAGER.
π Example Repo: minishlab/my-vicinity-repo
π§βπ« KNN, HNSW, USEARCH, ANNOY, PYNNDESCENT, FAISS, and VOYAGER.
π Example Repo: minishlab/my-vicinity-repo