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Language Dexterity Benchmark
I am working on a new benchmark to establish human language dexterity. My hypothesis is that certain language allow for more accurate dexterous behaviour - Pointed, unambigous, and confusion-free references of parts of speech in small and large contexts. There are certain languages with high degree of accurate grammar like Sanskrit, Esperanto, and Turkish. I am native Sanskrit speaker.
I have plans to establish this benchmark and test this hypothesis across 100 langauges. I have created 25 task prompts for text, image, video and robotics manipulation. We can test langauges across multiple popular models. Here is the github link: https://github.com/ParamTatva-org/Linguistic-Dexterity-Benchmark
I am working on a new benchmark to establish human language dexterity. My hypothesis is that certain language allow for more accurate dexterous behaviour - Pointed, unambigous, and confusion-free references of parts of speech in small and large contexts. There are certain languages with high degree of accurate grammar like Sanskrit, Esperanto, and Turkish. I am native Sanskrit speaker.
I have plans to establish this benchmark and test this hypothesis across 100 langauges. I have created 25 task prompts for text, image, video and robotics manipulation. We can test langauges across multiple popular models. Here is the github link: https://github.com/ParamTatva-org/Linguistic-Dexterity-Benchmark
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- 1 reply
reacted to
singhsidhukuldeep's
post with โค๏ธ
over 1 year ago
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1784
How many times have you said Pandas is slow and still kept on using it? ๐ผ๐จ
Get ready to say Pandas can be fast but it's expensive ๐
๐ Original Limitations:
๐ป CPU-Bound Processing: Traditional pandas operations are CPU-bound (mostly single-threaded๐ฐ), leading to slower processing of large datasets.
๐ง Memory Constraints: Handling large datasets in memory-intensive operations can lead to inefficiencies and limitations.
๐ฃ Achievements with @nvidia RAPIDS cuDF:
๐ GPU Acceleration: RAPIDS cuDF leverages GPU computing. Users switch to GPU-accelerated operations without modifying existing pandas code.
๐ Unified Workflows: Seamlessly integrates GPU and CPU operations, falling back to CPU when necessary.
๐ Optimized Performance: With extreme parallel operation opportunity of GPUs, this achieves up to 150x speedup in data processing, demonstrated through benchmarks like DuckDB.
๐ New Limitations:
๐ฎ GPU Availability: Requires a GPU (not everything should need a GPU)
๐ Library Compatibility: Currently in the initial stages, all the functionality cannot be ported
๐ข Data Transfer Overhead: Moving data between CPU and GPU can introduce latency if not managed efficiently. As some operations still run on the CPU.
๐ค User Adoption: We already had vectorization support in Pandas, people just didn't use it as it was difficult to implement. We already had DASK for parallelization. It's not that solutions didn't exist
Blog: https://developer.nvidia.com/blog/rapids-cudf-accelerates-pandas-nearly-150x-with-zero-code-changes/
For Jupyter Notebooks:
For python scripts:
Get ready to say Pandas can be fast but it's expensive ๐
๐ Original Limitations:
๐ป CPU-Bound Processing: Traditional pandas operations are CPU-bound (mostly single-threaded๐ฐ), leading to slower processing of large datasets.
๐ง Memory Constraints: Handling large datasets in memory-intensive operations can lead to inefficiencies and limitations.
๐ฃ Achievements with @nvidia RAPIDS cuDF:
๐ GPU Acceleration: RAPIDS cuDF leverages GPU computing. Users switch to GPU-accelerated operations without modifying existing pandas code.
๐ Unified Workflows: Seamlessly integrates GPU and CPU operations, falling back to CPU when necessary.
๐ Optimized Performance: With extreme parallel operation opportunity of GPUs, this achieves up to 150x speedup in data processing, demonstrated through benchmarks like DuckDB.
๐ New Limitations:
๐ฎ GPU Availability: Requires a GPU (not everything should need a GPU)
๐ Library Compatibility: Currently in the initial stages, all the functionality cannot be ported
๐ข Data Transfer Overhead: Moving data between CPU and GPU can introduce latency if not managed efficiently. As some operations still run on the CPU.
๐ค User Adoption: We already had vectorization support in Pandas, people just didn't use it as it was difficult to implement. We already had DASK for parallelization. It's not that solutions didn't exist
Blog: https://developer.nvidia.com/blog/rapids-cudf-accelerates-pandas-nearly-150x-with-zero-code-changes/
For Jupyter Notebooks:
%load_ext cudf.pandas
import pandas as pdFor python scripts:
python -m cudf.pandas script.py
replied to
their
post
over 1 year ago
Cute scholarly noise. Here is the link https://www.thehitavada.com/Encyc/2021/8/22/The-status-and-place-of-Sanskrit-language-in-modern-India.amp.html. What a dictionary has is not all for Sanskrit especially because the effort has been always siloed. Anyways, the logic of compunding and infliction can render most of the dictionaries to a few pages.
reacted to
radames's
post with ๐ฅ
over 1 year ago
Post
2170
AI-town now runs on Hugging Face Spaces with our API for LLMs and embeddings, including the open-source Convex backend, all in one container. Easy to duplicate and config on your own
Demo: radames/ai-town
Instructions: https://github.com/radames/ai-town-huggingface
Demo: radames/ai-town
Instructions: https://github.com/radames/ai-town-huggingface
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reacted to
awacke1's
post with ๐ฅ
over 1 year ago
Post
2492
I just completed getting all four aspects of the new OpenAI GPT-4-o Omni model to process Text, Image, Audio, and Video.
Check it out and let me know what you think!
Space: awacke1/GPT-4o-omni-text-audio-image-video
Discussion: awacke1/GPT-4o-omni-text-audio-image-video
Test Runs for All Four Modalities: awacke1/GPT-4o-omni-text-audio-image-video#1
--Aaron - @awacke1
Check it out and let me know what you think!
Space: awacke1/GPT-4o-omni-text-audio-image-video
Discussion: awacke1/GPT-4o-omni-text-audio-image-video
Test Runs for All Four Modalities: awacke1/GPT-4o-omni-text-audio-image-video#1
--Aaron - @awacke1
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- 4 replies
