raincandy-u/chinese-o3
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raincandy_U
raincandy-u
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published
a dataset
about 22 hours ago
raincandy-u/chinese-o3
updated
a dataset
1 day ago
raincandy-u/r1_20k_high_score_data
published
a dataset
1 day ago
raincandy-u/r1_20k_high_score_data
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raincandy-u's activity
published
a
dataset
about 22 hours ago
updated
a
dataset
1 day ago
published
a
dataset
1 day ago
reacted to
beomi's
post with ๐
5 months ago
Post
6447
# PyTorch == 2.5.0 Breaks Transformers' SDPAttention!
When you encounter "RuntimeError: cuDNN Frontend error: [cudnn_frontend] Error: No execution plans support the graph."
We can use workaround like this:
but this slow downs the performance gain from PyTorch 2.5.
Although it is fixed(not "fixed" but default option is turn-off the cuDNN SDPA) at here -- https://github.com/pytorch/pytorch/pull/138587 , but not released yet. (you need to install directly from source)
Fastest way for now : pip install "torch<2.5"
Ref: https://github.com/huggingface/diffusers/issues/9704#issuecomment-2422585273
When you encounter "RuntimeError: cuDNN Frontend error: [cudnn_frontend] Error: No execution plans support the graph."
We can use workaround like this:
torch.backends.cuda.enable_cudnn_sdp(False)but this slow downs the performance gain from PyTorch 2.5.
Although it is fixed(not "fixed" but default option is turn-off the cuDNN SDPA) at here -- https://github.com/pytorch/pytorch/pull/138587 , but not released yet. (you need to install directly from source)
Fastest way for now : pip install "torch<2.5"
Ref: https://github.com/huggingface/diffusers/issues/9704#issuecomment-2422585273
replied to
takeraparterer's
post
5 months ago
What base model are you using?
reacted to
takeraparterer's
post with ๐
5 months ago
Post
2287
Check this out: I trained an AI on huggingface posts! all of these are AI generated:
----------
Hello!
I'm excited to share that my colleague @felipeebert and I have released the largest Spanish LLM benchmark to date.
We've developed the Spanish LLM Evaluation Benchmark (SLAB), a set of benchmarks designed to evaluate the ability of language models to understand, generate and translate in Spanish.
SLAB includes five different benchmarks:
- Sentiment Analysis: evaluate models' ability to detect and describe sentiment in natural language
- Fact Checking: evaluate models' ability to detect and refute factual errors in text
- Question Answering: evaluate models' ability to answer questions in Spanish
- Open-ended Questions: evaluate models' ability to generate coherent responses in Spanish
- Translation: evaluate models' ability to translate in Spanish
SLAB is aligned with the latest Spanish LLM industry developments and includes the most recent models available on the market. We aim to keep our benchmarks up-to-date and relevant to the Spanish language ecosystem.
SLAB is available at: https://huggingface.co/datasets/argilla/SLAB.
If you would like to collaborate on building additional Spanish LLM benchmarks, let's discuss in the comments.
๐ SLAB Blog Post: https://argilla.com/blog/slab
----------
Hello everyone,
I'm thrilled to announce the release of
https://huggingface.co/01-AI/01AI-GPT-4o -
A new family of models that brings the power of transformer AI to the masses.
This model is designed to be accessible and easy to use, while still offering high-quality results.
Key features:
- Small model size: only 23M parameters
- Supports text generation, image generation, and text-to-image tasks
- Data-efficient training with a lightweight tokenizer
- Optimized for efficient on-device usage
- Uses the powerful transformer architecture to deliver high-quality results
Excited to see what you all think!
https://huggingface.co/01-AI/01AI-GPT-4o
----------
Hello!
I'm excited to share that my colleague @felipeebert and I have released the largest Spanish LLM benchmark to date.
We've developed the Spanish LLM Evaluation Benchmark (SLAB), a set of benchmarks designed to evaluate the ability of language models to understand, generate and translate in Spanish.
SLAB includes five different benchmarks:
- Sentiment Analysis: evaluate models' ability to detect and describe sentiment in natural language
- Fact Checking: evaluate models' ability to detect and refute factual errors in text
- Question Answering: evaluate models' ability to answer questions in Spanish
- Open-ended Questions: evaluate models' ability to generate coherent responses in Spanish
- Translation: evaluate models' ability to translate in Spanish
SLAB is aligned with the latest Spanish LLM industry developments and includes the most recent models available on the market. We aim to keep our benchmarks up-to-date and relevant to the Spanish language ecosystem.
SLAB is available at: https://huggingface.co/datasets/argilla/SLAB.
If you would like to collaborate on building additional Spanish LLM benchmarks, let's discuss in the comments.
๐ SLAB Blog Post: https://argilla.com/blog/slab
----------
Hello everyone,
I'm thrilled to announce the release of
https://huggingface.co/01-AI/01AI-GPT-4o -
A new family of models that brings the power of transformer AI to the masses.
This model is designed to be accessible and easy to use, while still offering high-quality results.
Key features:
- Small model size: only 23M parameters
- Supports text generation, image generation, and text-to-image tasks
- Data-efficient training with a lightweight tokenizer
- Optimized for efficient on-device usage
- Uses the powerful transformer architecture to deliver high-quality results
Excited to see what you all think!
https://huggingface.co/01-AI/01AI-GPT-4o
-
- 18 replies
reacted to
zamal's
post with ๐ฅ
5 months ago
Post
2087
Hello, lovely community! ๐
zamal/Molmo-4bit Thrilled to announce that the Molmo 7B 4-bit Space is now live! ๐ The model size has been reduced by six times with almost no performance loss, and the results will leave you amazed!
It runs on zero GPU, making it incredibly accessible for everyone!
Check it out here and start exploring today!
Happy experimenting! ๐
zamal/Molmo-4bit Thrilled to announce that the Molmo 7B 4-bit Space is now live! ๐ The model size has been reduced by six times with almost no performance loss, and the results will leave you amazed!
It runs on zero GPU, making it incredibly accessible for everyone!
Check it out here and start exploring today!
Happy experimenting! ๐
liked
a
model
8 months ago
upvoted
a
paper
9 months ago
My Ultra 9 185h run it in only 12tok/s. Anything wrong?
#1 opened 9 months ago
by
raincandy-u
updated
a
model
9 months ago
liked
a
Space
9 months ago
upvoted
a
paper
9 months ago
liked
a
Space
9 months ago
[bot] Conversion to Parquet
#1 opened 10 months ago
by
parquet-converter
liked
a
model
10 months ago
reacted to
Draichi's
post with ๐ค
10 months ago
Post
2286
Hey Hugging Face Community ๐ค
I'm excited to share my latest project that combines my passion for deep learning and racing cars. I recently created a simple method to predict Formula 1 lap times using machine learning . This is the first solution of its kind in the open-source community, and I'm thrilled to present it to you all.
๐๏ธ The project leverages historical telemetry data to predict lap times, providing a new tool for race strategy and performance analysis. You can check out the notebook on Kaggle here https://www.kaggle.com/code/lucasdraichi/hamilton-lap-time-prediction and see the detailed breakdown of the model and its predictions.
I invite you all to take a look at the lap time predictor, provide feedback, and join the discussion. Your insights and participation would be invaluable as we continue to develop and enhance these tools.
Let's push the boundaries of what's possible with AI in motorsports together!
I'm excited to share my latest project that combines my passion for deep learning and racing cars. I recently created a simple method to predict Formula 1 lap times using machine learning . This is the first solution of its kind in the open-source community, and I'm thrilled to present it to you all.
๐๏ธ The project leverages historical telemetry data to predict lap times, providing a new tool for race strategy and performance analysis. You can check out the notebook on Kaggle here https://www.kaggle.com/code/lucasdraichi/hamilton-lap-time-prediction and see the detailed breakdown of the model and its predictions.
I invite you all to take a look at the lap time predictor, provide feedback, and join the discussion. Your insights and participation would be invaluable as we continue to develop and enhance these tools.
Let's push the boundaries of what's possible with AI in motorsports together!
- 3 replies
reacted to
their
post with ๐คฏ๐
10 months ago
Post
2426
๐ค I trained what is probably the smallest (600k ~) TinyStories model! It really can write grammatically correct stories!
raincandy-u/TinyStories-656K
Try this space based on this minuscule model!
raincandy-u/Story-Teller
Edit: Moreover, the model weight size is only 1.31MB under bf16, and can be reduced to the 700KB level when using Q8_0 quantization Uโขใงโข*U
Edit: Now 1000K params chat model!
raincandy-u/TinyChat-1776K
raincandy-u/TinyStories-656K
Try this space based on this minuscule model!
raincandy-u/Story-Teller
Edit: Moreover, the model weight size is only 1.31MB under bf16, and can be reduced to the 700KB level when using Q8_0 quantization Uโขใงโข*U
Edit: Now 1000K params chat model!
raincandy-u/TinyChat-1776K
-
- 2 replies