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Zilikon

Zilikon
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reacted to Xenova's post with 🔥 11 days ago
First project of 2025: Vision Transformer Explorer I built a web app to interactively explore the self-attention maps produced by ViTs. This explains what the model is focusing on when making predictions, and provides insights into its inner workings! 🤯 Try it out yourself! 👇 https://huggingface.co./spaces/webml-community/attention-visualization Source code: https://github.com/huggingface/transformers.js-examples/tree/main/attention-visualization
reacted to s-emanuilov's post with 👀 11 days ago
Hey HF community! 👋 Excited to share Monkt - a tool I built to solve the eternal headache of processing documents for ML/AI pipelines. What it does: Converts PDFs, Word, PowerPoint, Excel, Web pages or raw HTML into clean Markdown or structured JSON. Great for: ✔ LLM training dataset preparation; ✔ Knowledge base construction; ✔ Research paper processing; ✔ Technical documentation management. It has API access for integration into ML pipelines. Check it out at https://monkt.com/ if you want to save time on document processing infrastructure. Looking forward to your feedback!
liked a model 15 days ago
black-forest-labs/FLUX.1-schnell
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reacted to Xenova's post with 🔥 11 days ago
reacted to s-emanuilov's post with 👀 11 days ago
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Hey HF community! 👋

Excited to share Monkt - a tool I built to solve the eternal headache of processing documents for ML/AI pipelines.

What it does: Converts PDFs, Word, PowerPoint, Excel, Web pages or raw HTML into clean Markdown or structured JSON.

Great for:
✔ LLM training dataset preparation;
✔ Knowledge base construction;
✔ Research paper processing;
✔ Technical documentation management.

It has API access for integration into ML pipelines.

Check it out at https://monkt.com/ if you want to save time on document processing infrastructure.

Looking forward to your feedback!
  • 3 replies
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liked 2 models 15 days ago
liked a dataset 15 days ago
liked a model 15 days ago
New activity in deepseek-ai/DeepSeek-V3-Base 15 days ago

Confusing Answer

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#36 opened 15 days ago by
Zilikon
reacted to lewtun's post with 🔥 25 days ago
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We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute 🔥

How? By combining step-wise reward models with tree search algorithms :)

We show that smol models can match or exceed the performance of their much larger siblings when given enough "time to think"

We're open sourcing the full recipe and sharing a detailed blog post.

In our blog post we cover:

📈 Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time.

🎄 Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique. This simple yet effective method improves diversity and delivers better performance, particularly at large test-time compute budgets.

🧭 Search and Learn: A lightweight toolkit for implementing search strategies with LLMs and built for speed with vLLM

Here's the links:

- Blog post: HuggingFaceH4/blogpost-scaling-test-time-compute

- Code: https://github.com/huggingface/search-and-learn

Enjoy!
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updated 2 models almost 2 years ago