Junghwan Park

9bow

https://9bow.io

9bow

AI & ML interests

time-series forecasting

Recent Activity

upvoted a paper 23 days ago

KORMo: Korean Open Reasoning Model for Everyone

liked a Space 12 months ago

VIDraft/eum

upvoted a paper about 1 year ago

Apollo: An Exploration of Video Understanding in Large Multimodal Models

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upvoted a paper 23 days ago

KORMo: Korean Open Reasoning Model for Everyone

Paper • 2510.09426 • Published Oct 10 • 83

liked a Space 12 months ago

Open Perflexity V2

🏢

181

LLM service based on Search and Vector enhanced retrieval

upvoted a paper about 1 year ago

Apollo: An Exploration of Video Understanding in Large Multimodal Models

Paper • 2412.10360 • Published Dec 13, 2024 • 147

liked 2 models over 1 year ago

UCSC-VLAA/ViT-L-16-HTxt-Recap-CLIP

Zero-Shot Image Classification • Updated Jun 24, 2024 • 131 • 17

internlm/internlm-xcomposer2-vl-7b

Visual Question Answering • Updated Apr 12, 2024 • 7.78k • 83

liked a Space over 1 year ago

Open VLM Leaderboard

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952

VLMEvalKit Evaluation Results Collection

reacted to thomwolf's post with 👍❤️🔥 over 1 year ago

Post

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A Little guide to building Large Language Models in 2024

This is a post-recording of a 75min lecture I gave two weeks ago on how to train a LLM from scratch in 2024. I tried to keep it short and comprehensive – focusing on concepts that are crucial for training good LLM but often hidden in tech reports.

In the lecture, I introduce the students to all the important concepts/tools/techniques for training good performance LLM:
* finding, preparing and evaluating web scale data
* understanding model parallelism and efficient training
* fine-tuning/aligning models
* fast inference

There is of course many things and details missing and that I should have added to it, don't hesitate to tell me you're most frustrating omission and I'll add it in a future part. In particular I think I'll add more focus on how to filter topics well and extensively and maybe more practical anecdotes and details.

Now that I recorded it I've been thinking this could be part 1 of a two-parts series with a 2nd fully hands-on video on how to run all these steps with some libraries and recipes we've released recently at HF around LLM training (and could be easily adapted to your other framework anyway):
*datatrove for all things web-scale data preparation: https://github.com/huggingface/datatrove
*nanotron for lightweight 4D parallelism LLM training: https://github.com/huggingface/nanotron
*lighteval for in-training fast parallel LLM evaluations: https://github.com/huggingface/lighteval

Here is the link to watch the lecture on Youtube: https://www.youtube.com/watch?v=2-SPH9hIKT8
And here is the link to the Google slides: https://docs.google.com/presentation/d/1IkzESdOwdmwvPxIELYJi8--K3EZ98_cL6c5ZcLKSyVg/edit#slide=id.p

Enjoy and happy to hear feedback on it and what to add, correct, extend in a second part.

2 replies

liked a dataset almost 2 years ago

MBZUAI/VideoInstruct-100K

Viewer • Updated Sep 29, 2023 • 100k • 175 • 45

liked a model almost 2 years ago

vdo/Video-LLaMA-Series

Visual Question Answering • Updated Jun 14, 2023 • 10

reacted to wvaneaton's post with 👍 almost 2 years ago

Post

We fine-tuned 25 mistral-7b models that outperform GPT-4 on task-specific use cases!

Check them out at LoRA Land: https://pbase.ai/3uFh7Qc

You can prompt them and compare their results to mistral-7b-instruct in real time!

They're also on HF for you to play with:

predibase

Let us know what you think!

upvoted a paper over 2 years ago

On the Origin of LLMs: An Evolutionary Tree and Graph for 15,821 Large Language Models

Paper • 2307.09793 • Published Jul 19, 2023 • 47

liked 7 models over 2 years ago