Quanto: a pytorch quantization toolkit
Quantization is a technique to reduce the computational and memory costs of evaluating Deep Learning Models by representing their weights and activations with low-precision data types like 8-bit integer (int8) instead of the usual 32-bit floating point (float32). Reducing the number of bits means the resulting model requires less memory storage, which is crucial for deploying Large Language Models
Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL
Unsloth was benchmarked across 59 runs using 4 datasets on Tesla T4 and A100 Google Colab instances. QLoRA was applied to all linear layers (attention and MLP) with a rank of 16, and gradient checkpointing was on. By testing against the latest Transformers version (4.36), which has SDPA natively integrated if you have Pytorch 2.1.1, Unsloth is up to 2.7x faster and uses up to 74% less memory. We a
Paper page - Optimized Network Architectures for Large Language Model Training with Billions of Parameters
Abstract This paper challenges the well-established paradigm for building any-to-any networks for training Large Language Models (LLMs). We show that LLMs exhibit a unique communication pattern where only small groups of GPUs require high-bandwidth any-to-any communication within them, to achieve near-optimal training performance. Across these groups of GPUs, the communication is insignificant, sp
Optimizing your LLM in production
Note: This blog post is also available as a documentation page on Transformers. Large Language Models (LLMs) such as GPT3/4, Falcon, and LLama are rapidly advancing in their ability to tackle human-centric tasks, establishing themselves as essential tools in modern knowledge-based industries. Deploying these models in real-world tasks remains challenging, however: To exhibit near-human text unders
Open LLM Leaderboard - a Hugging Face Space by HuggingFaceH4
Track, rank and evaluate open LLMs and chatbots
Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA
LLMs are known to be large, and running or training them in consumer hardware is a huge challenge for users and accessibility. Our LLM.int8 blogpost showed how the techniques in the LLM.int8 paper were integrated in transformers using the bitsandbytes library. As we strive to make models even more accessible to anyone, we decided to collaborate with bitsandbytes again to allow users to run models
nvidia/GPT-2B-001 · Hugging Face
GPT-2B-001 ||| Model Description GPT-2B-001 is a transformer-based language model. GPT refers to a class of transformer decoder-only models similar to GPT-2 and 3 while 2B refers to the total trainable parameter count (2 Billion) [1, 2]. This model was trained on 1.1T tokens with NeMo. Model Architecture improvements The model uses the SwiGLU activation function [4] Rotary positional embeddings (R
A Gentle Introduction to 8-bit Matrix Multiplication for transformers at scale using transformers, accelerate and bitsandbytes
The 3 models are BLOOM-176B, T5-11B and T5-3B. Hugging Face transformers integration nuances Next let's discuss the specifics of the Hugging Face transformers integration. Let's look at the usage and the common culprit you may encounter while trying to set things up. Usage The module responsible for the whole magic described in this blog post is called Linear8bitLt and you can easily import it fro
Introduction to Graph Machine Learning
In this blog post, we cover the basics of graph machine learning. We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how people learn on graphs, from pre-neural methods (exploring graph features at the same time) to what are commonly called Graph Neural Networks. Lastly, we peek into the world of Transformers for graphs. Graphs What is a graph?
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Distributed Inference with 🤗 Accelerate
Large-scale Near-deduplication Behind BigCode
Paper page - Full Parameter Fine-tuning for Large Language Models with Limited Resources
Paper page - Orca: Progressive Learning from Complex Explanation Traces of GPT-4
PEFT
replit/replit-code-v1-3b · Hugging Face
Distributed training with 🤗 Accelerate
From PyTorch DDP to Accelerate to Trainer, mastery of distributed training with ease
timm
DeepSpeed
Tf Xla Generate Benchmarks - a Hugging Face Space by joaogante