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raincandy_U

raincandy-u

AI & ML interests

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Recent Activity

Reacted to beomi's post with 😎 about 1 month ago

# 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: ```python 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 about 2 months ago

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. 🔗 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

Reacted to takeraparterer's post with 👀 about 2 months ago

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Reacted to beomi's post with 😎 about 1 month ago

Post

3815

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 about 2 months ago

What base model are you using?

Reacted to takeraparterer's post with 👀 about 2 months ago

Post

2239

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.

🔗 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

4 replies

Reacted to zamal's post with 🔥 about 2 months ago

Post

2020

Hello, lovely community! 🌟

zamal/Molmo-4bit Thrilled to announce that the Molmo 7B 4-bit Space is now live! 🚀 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! 🎉

liked a model 4 months ago

xinsir/controlnet-union-sdxl-1.0

Text-to-Image • Updated Jul 30 • 81.8k • 1.12k

upvoted a paper 5 months ago

T-FREE: Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings

Paper • 2406.19223 • Published Jun 27 • 8

New activity in rajatkrishna/Meta-Llama-3-8B-OpenVINO-INT4 5 months ago

My Ultra 9 185h run it in only 12tok/s. Anything wrong?

#1 opened 5 months ago by

raincandy-u

updated a model 5 months ago

raincandy-u/Qwen2-7B-Instruct-OpenVINO-int4

Updated Jun 21

liked a Space 5 months ago

Runtime error

🌿

DesignEdit

upvoted a paper 5 months ago

Needle In A Multimodal Haystack

Paper • 2406.07230 • Published Jun 11 • 52

liked a Space 5 months ago

Running

💬

Chat Template Viewer

New activity in raincandy-u/TinyChat 6 months ago

[bot] Conversion to Parquet

#1 opened 6 months ago by

parquet-converter

liked a model 6 months ago

mradermacher/TinyStories-656K-GGUF

Updated Jun 13 • 142 • 1

upvoted an article 6 months ago

Article

Uncensor any LLM with abliteration

•

Jun 13

• 371

Reacted to Draichi's post with 🤗 6 months ago

Post

2273

Hey Hugging Face Community 🤗

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.

🏎️ 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 🤯👀 6 months ago

Post

2332

🤗 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 U•ェ•*U

Edit: Now 1000K params chat model!

raincandy-u/TinyChat-1776K

2 replies

liked a model 6 months ago

raincandy-u/TinyChat-1776K

Text Generation • Updated Jun 13 • 124 • 5

updated a model 6 months ago

raincandy-u/TinyChat-1776K

Text Generation • Updated Jun 13 • 124 • 5

updated a dataset 6 months ago

raincandy-u/TinyChat

Viewer • Updated Jun 13 • 100k • 39 • 1