AI & ML interests

Superintelligence Alignment

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louisbrulenaudet 
posted an update 5 days ago
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2616
Because hackathons are often the starting point for many AI projects, I've created a Python-backend template incorporating my feedback to streamline collaboration and urgent deployments 🏎️

Within a year, I had the opportunity to participate in hackathons organized by Mistral, OpenAI, and DeepMind and this GitHub template is structured around several fundamental building blocks and recommendations I offer developers eager to participate in their first hackathon, whether as part of a team or individually. Its emphasis is on rapid setup and deployment through:
- uv as a package manager, simplifying usage via a series of pre-configured make commands.
- FastAPI for API management, structured in a modular architecture designed to minimize branch conflicts during merges to main branches (using minimal health-check and ping routes to verify Docker’s proper execution and backend accessibility on the local network).
- Pydantic for validation and type handling, which simplifies debugging and enhances understanding of data objects.
- A set of custom instructions tailored for agents (Cline and GitHub Copilot), aimed at improving overall comprehension of the application and optimizing the vibe-coding experience.

This template includes unit tests with a 100% success rate and test coverage, as well as a minimal CI file ensuring that the FastAPI application runs correctly. Thus, merging code that breaks the server into production becomes impossible ⛔️

In general, I would reiterate an essential piece of advice: your two main adversaries are branch conflicts—particularly when the same file is modified concurrently within a brief period, especially if your architecture isn’t built for scalability—and deployment issues under urgent circumstances ⏱️

Link to GitHub: https://github.com/louisbrulenaudet/hackathon-backend

Simply issue these commands and you can ship your code at the speed of light:
make init
make dev
Nymbo 
posted an update 14 days ago
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1729
Anyone know how to reset Claude web's MCP config? I connected mine when the HF MCP first released with just the default example spaces added. I added lots of other MCP spaces but Claude.ai doesn't update the available tools... "Disconnecting" the HF integration does nothing, deleting it and adding it again does nothing.

Refreshing tools works fine in VS Code because I can manually restart it in mcp.json, but claude.ai has no such option. Anyone got any ideas?
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louisbrulenaudet 
posted an update 25 days ago
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1070
🌐 Clinical Trials Dataset now available on Hugging Face! 🧬

I’ve just released a comprehensive, ML-ready dataset featuring 500,000+ clinical trial records sourced directly from ClinicalTrials.gov for biomedical NLP, healthcare analytics, and clinical research applications 🤗

I wanted to produce the most complete and up-to-date dump with all raw data partially flattened to simplify extraction, self-querying and processing.

Do you have any ideas about what we can do with it? Using descriptions to enhance specialized embedding models?

louisbrulenaudet/clinical-trials
Nymbo 
posted an update 2 months ago
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3303
Haven't seen this posted anywhere - Llama-3.3-8B-Instruct is available on the new Llama API. Is this a new model or did someone mislabel Llama-3.1-8B?
  • 1 reply
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Nymbo 
posted an update 2 months ago
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2732
PSA for anyone using Nymbo/Nymbo_Theme or Nymbo/Nymbo_Theme_5 in a Gradio space ~

Both of these themes have been updated to fix some of the long-standing inconsistencies ever since the transition to Gradio v5. Textboxes are no longer bright green and in-line code is readable now! Both themes are now visually identical across versions.

If your space is already using one of these themes, you just need to restart your space to get the latest version. No code changes needed.
louisbrulenaudet 
posted an update 4 months ago
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1214
I’ve just released logfire-callback on PyPI, designed to facilitate monitoring of Hugging Face Transformer training loops using Pydantic Logfire 🤗

The callback will automatically log training start with configuration parameters, periodic metrics and training completion ⏱️

Install the package using pip:
pip install logfire-callback

First, ensure you have a Logfire API token and set it as an environment variable:
export LOGFIRE_TOKEN=your_logfire_token

Then use the callback in your training code:
from transformers import Trainer, TrainingArguments
from logfire_callback import LogfireCallback

# Initialize your model, dataset, etc.

training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    # ... other training arguments
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    callbacks=[LogfireCallback()]  # Add the Logfire callback here
)

trainer.train()

If you have any feedback, please reach out at @louisbrulenaudet
not-lain 
posted an update 4 months ago
louisbrulenaudet 
posted an update 5 months ago
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3471
I am pleased to introduce my first project built upon Hugging Face’s smolagents framework, integrated with Alpaca for financial market analysis automation 🦙🤗

The project implements technical indicators such as the Relative Strength Index (RSI) and Bollinger Bands to provide momentum and volatility analysis. Market data is retrieved through the Alpaca API, enabling access to historical price information across various timeframes.

AI-powered insights are generated using Hugging Face’s inference API, facilitating the analysis of market trends through natural language processing with DuckDuckGo search integration for real-time sentiment analysis based on financial news 🦆

Link to the GitHub project: https://github.com/louisbrulenaudet/agentic-market-tool

not-lain 
posted an update 6 months ago
not-lain 
posted an update 6 months ago
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1785
we now have more than 2000 public AI models using ModelHubMixin🤗
not-lain 
posted an update 6 months ago
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4132
Published a new blogpost 📖
In this blogpost I have gone through the transformers' architecture emphasizing how shapes propagate throughout each layer.
🔗 https://huggingface.co/blog/not-lain/tensor-dims
some interesting takeaways :