johnowhitaker/pyramid_noise_test_600steps_08discount
Text-to-Image
•
Updated
•
5
•
9
Zilikon
Zilikon
AI & ML interests
None yet
Recent Activity
liked
a model
17 days ago
johnowhitaker/pyramid_noise_test_600steps_08discount
reacted
to
s-emanuilov's
post
with 👀
about 2 months 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!
Organizations
None yet
Zilikon's activity
liked
a
model
17 days ago
reacted to
Xenova's
post with 🔥
about 2 months ago
Post
8324
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! 👇
webml-community/attention-visualization
Source code: https://github.com/huggingface/transformers.js-examples/tree/main/attention-visualization
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! 👇
webml-community/attention-visualization
Source code: https://github.com/huggingface/transformers.js-examples/tree/main/attention-visualization
reacted to
s-emanuilov's
post with 👀
about 2 months ago
Post
2578
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!
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
liked
2
models
2 months ago
liked
a
dataset
2 months ago
liked
a
model
2 months ago
Confusing Answer
3
#36 opened 2 months ago
by
Zilikon
reacted to
lewtun's
post with 🔥
2 months ago
Post
6913
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!
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!
- 2 replies
updated
2
models
almost 2 years ago