FinMind
blog
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FinMind | blog | |
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FinMind
blog
- Refact LLM: New 1.6B code model reaches 32% HumanEval and is SOTA for the size
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A comprehensive guide to running Llama 2 locally
If you just want to do inference/mess around with the model and have a 16GB GPU, then this[0] is enough to paste into a notebook. You need to have access to the HF models though.
0. https://github.com/huggingface/blog/blob/main/llama2.md#usin...
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Let’s train your first Offline Decision Transformer model from scratch 🤖
The hands-on 👉https://github.com/huggingface/blog/blob/main/notebooks/101_train-decision-transformers.ipynb
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How to switch to half precision fp16?
I'm also running the optimized script but it doesn't run with 512x512 on my RTX3050 Ti mobile. On this website, they recommend to switch to fp16 for GPUs with less than 10gb of vram.
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Are people hiding their deep learning code?
Here's a notebook illustrating how to train a language model from scratch: https://github.com/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb
What are some alternatives?
alphalens - Performance analysis of predictive (alpha) stock factors
text-generation-inference - Large Language Model Text Generation Inference
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
gds_env - A containerised platform for Geographic Data Science
awesome-notebooks - A powerful data & AI notebook templates catalog: prompts, plugins, models, workflow automation, analytics, code snippets - following the IMO framework to be searchable and reusable in any context.
python-training - Python training for business analysts and traders
QuantumKatas - Tutorials and programming exercises for learning Q# and quantum computing
AIrsenal - Machine learning Fantasy Premier League team
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM
simfin-tutorials - Tutorials for SimFin - Simple financial data for Python
Practical_RL - A course in reinforcement learning in the wild