Robust automatic brain vessel segmentation in 3D CTA scans using dynamic 4D-CTA data
Paper
•
2602.00391
•
Published
AI & ML interests
Democratizar el PLN en español creando recursos abiertos en nuestro idioma🚀
Recent Activity
View all activity
alceballosa
authored
a
paper
6 days ago
Post
208
Last week,
zai-org
dropped
zai-org/GLM-4.7-Flash. Now, we bring it to Microsoft Foundry!
- 🏆 30B-A3B MoE, the strongest model in the 30B class. It excels at coding tasks, agentic workflows and reasoning.
- 🤏🏻 Lighter version of his 358B big brother, balancing performance and efficiency.
Not light enough for you? We are also adding
unsloth
unsloth/GLM-4.7-Flash-GGUF to the catalog, with GPU and CPU support powered by llama.cpp 🔥
Go join the hype and deploy them from the Hugging Face collection on Microsoft Foundry!
- 🏆 30B-A3B MoE, the strongest model in the 30B class. It excels at coding tasks, agentic workflows and reasoning.
- 🤏🏻 Lighter version of his 358B big brother, balancing performance and efficiency.
Not light enough for you? We are also adding
Go join the hype and deploy them from the Hugging Face collection on Microsoft Foundry!
- 2 replies
alvarobartt
posted
an
update
14 days ago
Post
2912
💥
💡 Alternatively, you can also set the
hf-mem v0.4.1 now also estimates KV cache memory requirements for any context length and batch size with the --experimental flag!uvx hf-mem --model-id ... --experimental will automatically pull the required information from the Hugging Face Hub to include the KV cache estimation, when applicable.💡 Alternatively, you can also set the
--max-model-len, --batch-size and --kv-cache-dtype arguments (à la vLLM) manually if preferred.
-
- 1 reply
Post
4232
- 3 replies
osanseviero
authored
a
paper
5 months ago
mariagrandury
authored
4
papers
5 months ago
Adding LLMs to the psycholinguistic norming toolbox: A practical guide to getting the most out of human ratings
Paper
•
2509.14405
•
Published
•
2
Psycholinguistic Word Features: a New Approach for the Evaluation of LLMs Alignment with Humans
Paper
•
2506.22439
•
Published
•
3
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Paper
•
2509.14233
•
Published
•
16
La Leaderboard: A Large Language Model Leaderboard for Spanish Varieties and Languages of Spain and Latin America
Paper
•
2507.00999
•
Published
•
1
Post
2883
Just applied for HF Community Grant for “Hugging Research” — a lightweight CodeAgent‑based research assistant built on Hugging Face’s Open Deep Research project for the Hugging Face Hub (models, datasets, Spaces, users, collections, papers). It gathers links via dedicated tools and organizes them for easy review.
As this is for the community, comments and suggestions are appreciated: daqc/hugging-research#1
As this is for the community, comments and suggestions are appreciated: daqc/hugging-research#1
mariagrandury
authored
2
papers
9 months ago
Post
939
Aspects of consciousness by Murray Shanahan:
- Awareness of the world (Perception)
- Self-awareness (own body, where it is in the space)
- Imagination / Stream of Consciousness (William James)
- Metacognition (Ability to think what we know)
- Emotions (Feel/Suffer, Sentient)
Source:
https://www.youtube.com/watch?v=v1Py_hWcmkU
- Awareness of the world (Perception)
- Self-awareness (own body, where it is in the space)
- Imagination / Stream of Consciousness (William James)
- Metacognition (Ability to think what we know)
- Emotions (Feel/Suffer, Sentient)
Source:
https://www.youtube.com/watch?v=v1Py_hWcmkU
osanseviero
authored
a
paper
11 months ago
alvarobartt
posted
an
update
12 months ago
Post
3636
🔥 Agents can do anything!
@microsoft
Research just announced the release of Magma 8B!
Magma is a new Visual Language Model (VLM) with 8B parameters for multi-modal agents designed to handle complex interactions across virtual and real environments; and it's MIT licensed!
Magma comes with exciting new features such as:
- Introduces the Set-of-Mark and Trace-of-Mark techniques for fine-tuning
- Leverages a large amount of unlabeled video data to learn the spatial-temporal grounding and planning
- A strong generalization and ability to be fine-tuned for other agentic tasks
- SOTA in different multi-modal benchmarks spanning across UI navigation, robotics manipulation, image / video understanding and spatial understanding and reasoning
- Generates goal-driven visual plans and actions for agentic use cases
Model: microsoft/Magma-8B
Technical Report: Magma: A Foundation Model for Multimodal AI Agents (2502.13130)
Magma is a new Visual Language Model (VLM) with 8B parameters for multi-modal agents designed to handle complex interactions across virtual and real environments; and it's MIT licensed!
Magma comes with exciting new features such as:
- Introduces the Set-of-Mark and Trace-of-Mark techniques for fine-tuning
- Leverages a large amount of unlabeled video data to learn the spatial-temporal grounding and planning
- A strong generalization and ability to be fine-tuned for other agentic tasks
- SOTA in different multi-modal benchmarks spanning across UI navigation, robotics manipulation, image / video understanding and spatial understanding and reasoning
- Generates goal-driven visual plans and actions for agentic use cases
Model: microsoft/Magma-8B
Technical Report: Magma: A Foundation Model for Multimodal AI Agents (2502.13130)
mariagrandury
authored
a
paper
over 1 year ago
alvarobartt
posted
an
update
over 1 year ago
Post
3038
🤗 Serving Meta Llama 3.1 405B on Google Cloud is now possible via the Hugging Face Deep Learning Containers (DLCs) for Text Generation Inference (TGI)
In this post, we showcase how to deploy https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-FP8 on an A3 instance with 8 x H100 GPUs on Vertex AI
Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as we enable more experiences to build AI with open models on Google Cloud!
Read the full post at https://huggingface.co/blog/llama31-on-vertex-ai
In this post, we showcase how to deploy https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-FP8 on an A3 instance with 8 x H100 GPUs on Vertex AI
Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as we enable more experiences to build AI with open models on Google Cloud!
Read the full post at https://huggingface.co/blog/llama31-on-vertex-ai
mariagrandury
authored
a
paper
over 1 year ago
Post
8234
🚨Exciting news for the Multilingual Synthetic Data Community!🚨
I’ve taken inspiration from the MAGPIE paper on Llama-3-8B-instruct and extended its capabilities. Here’s what’s new!
🗞 The MAGPIE paper showcased that if you use the instruction-tuned version (
🤔 While reading a script by Sebastian Raschka, PhD, I wondered: Could these advancements be replicated in other languages? Specifically, could they benefit non-English datasets?
🎉 And the answer is YES! At least for Spanish. I've successfully adapted the techniques for Spanish, proving the model's flexibility and multilingual capabilities.
👩💻 To make this accessible, I created a basic script (heavily inspired by the Sebastian Raschka one) that allows you to generate similar datasets using
[Script](https://gist.github.com/mrm8488/4650a5e3cc45523798a527a3446eb312)
🔍 Explore the datasets 📚 generated using our new script!
- [Llama-3-8B](https://huggingface.co/datasets/mrm8488/dataset_llama3_5000_samples_es_4231_filtered)
- [Phi-3-medium](https://huggingface.co/datasets/mrm8488/dataset_phi3-medium_5000_samples_es_3906_filtered)
- [Phi-3-mini](https://huggingface.co/datasets/mrm8488/dataset_phi3_5000_samples_es_3282_filtered)
Note: These datasets have basic filtering. Apply additional quality filters before using them to fine-tune large language models.
Inspiration and base script:
https://github.com/rasbt/LLMs-from-scratch/blob/main/ch07/05_dataset-generation/llama3-ollama.ipynb
https://www.linkedin.com/feed/update/urn:li:activity:7210982019751661568/
I’ve taken inspiration from the MAGPIE paper on Llama-3-8B-instruct and extended its capabilities. Here’s what’s new!
🗞 The MAGPIE paper showcased that if you use the instruction-tuned version (
Llama-3-8B-instruct) to generate synthetic instructions and then fine-tune the base version (Llama-3-8B) on this dataset, you can improve even the it-tuned version🤔 While reading a script by Sebastian Raschka, PhD, I wondered: Could these advancements be replicated in other languages? Specifically, could they benefit non-English datasets?
🎉 And the answer is YES! At least for Spanish. I've successfully adapted the techniques for Spanish, proving the model's flexibility and multilingual capabilities.
👩💻 To make this accessible, I created a basic script (heavily inspired by the Sebastian Raschka one) that allows you to generate similar datasets using
ollama models (initially phi and llama3) automatically and upload it to the Hugging Face Hub
🔍 Explore the datasets 📚 generated using our new script!
- [Llama-3-8B](https://huggingface.co/datasets/mrm8488/dataset_llama3_5000_samples_es_4231_filtered)
- [Phi-3-medium](https://huggingface.co/datasets/mrm8488/dataset_phi3-medium_5000_samples_es_3906_filtered)
- [Phi-3-mini](https://huggingface.co/datasets/mrm8488/dataset_phi3_5000_samples_es_3282_filtered)
Note: These datasets have basic filtering. Apply additional quality filters before using them to fine-tune large language models.
Inspiration and base script:
https://github.com/rasbt/LLMs-from-scratch/blob/main/ch07/05_dataset-generation/llama3-ollama.ipynb
https://www.linkedin.com/feed/update/urn:li:activity:7210982019751661568/
-
- 7 replies
mariagrandury
authored
a
paper
over 1 year ago