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Dataviz developer ✧ Freelance | Open for projects
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Assessing your cultural and operational fit
huggingface
Data Scientist
June 26, 2026 – Present
alignment-handbook
August 25, 2023 – Present
Robust recipes to align language models with human and AI preferences
View Projecttext-generation-inference
October 8, 2022 – Present
Large Language Model Text Generation Inference
View Projectdeep-rl-class
April 21, 2022 – Present
This repo contains the Hugging Face Deep Reinforcement Learning Course.
View Projectdatasets
March 26, 2020 – Present
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
View Projecttokenizers
November 1, 2019 – Present
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
View Projectpytorch-image-models
February 2, 2019 – Present
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
View Projecttransformers
October 29, 2018 – Present
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
View ProjectCultural Fit Analysis
The candidate's profile shows a strong alignment with a research-heavy, open-source, and innovation-driven culture, particularly within the AI/ML domain. Their extensive contributions to Hugging Face projects (transformers, datasets, text-generation-inference, tokenizers, alignment-handbook, deep-rl-class, notebooks) demonstrate a deep commitment to the ecosystem and a collaborative mindset. However, the lack of diverse project types outside of core ML/AI development might indicate a narrower focus, which could be a weakness depending on the specific team's needs for broader system design or cross-functional collaboration.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a strong technical focus and ability to contribute to complex open-source initiatives, but direct evidence of collaboration, problem-solving under pressure, or communication style is not available.