Llm

Learn how to fine-tune Llama 3 70b with PyTorch FSDP and Q-Lora using Hugging Face TRL, Transformers, PEFT and Datasets.

In this blog post you will learn how to deploy Llama 3 70B to Amazon SageMaker.

In this blog post you will learn how to accelerate Mixtral using Speculative Decoding (Medusa) and Quantization (AWQ).

In this blog post you will learn how to deploy Meta Llama 2 70B on AWS Inferentia2 with Hugging Face Optimum on Amazon SageMaker.

In this blog post you will learn how to fine-tune open LLMs from Hugging Face using Amazon SageMaker.

In this blog post you will learn how to evaluate LLMs using Hugging Face lighteval on Amazon SageMaker.

In this blog post you will learn how to fine tune Google Gemma using Hugging Face Transformers, Datasets and TRL.

In this blog post you will learn how to align LLMs using Hugging Face TRL and RLHF through Direct Preference Optimization (DPO).

In this blog post you will learn how to fine-tune LLMs using Hugging Face TRL, Transformers and Datasets in 2024. We will fine-tune a LLM on a text to SQL dataset.

In this blog post you will learn how to increase the throughput of Llama 13B on Amazon SageMaker using single instance multi-replica endpoints.

In this blog post you will learn how to fine-tune Llama 7B on AWS Trainium using the Hugging Face Optimum Neuron library.

In this blog post you will learn how to use the huggingface_hub library to create, send requests to, pause, and delete Hugging Face Inference Endpoints.

In this blog post you will learn how to deploy Mixtral 8x7B to Amazon SageMaker.

In this blog post I took a closer look at Amazon Bedrock Titan embeddings model and how good (bad) the perform.

Learn how to evaluate LLMs and RAG pipelines using Langchain and Hugging Face

Learn how to deploy Hugging Face Idefics 9B & 80B to Amazon SageMaker and send requests with images and text to the model.

Learn how to fine-tuned and deploy Mistral 7B with Hugging Face on Amazon SageMaker and leverage technique like Qlora, Flash Attention and response streaming

Benchmark evaluating varying sizes of Llama 2 on a range of Amazon EC2 instance types with different load levels on latency (ms per token), and throughput (tokens per second).

In this example we will show how to fine-tune Falcon 180B using DeepSpeed, Hugging Face Transformers, LoRA with Flash Attention on a multi-GPU machine.

Learn how to fine-tune Falcon 180B with QLoRA and Flash Attention on Amazon SageMaker.

Learn how to deploy Falcon 180B to Amazon SageMaker and how to create a chatbot with streaming inference.

Learn how to quantize Llama 2 7B with GPTQ to use 4x less memory.

Learn how to deploy Llama 2 models (7B - 70B) to Amazon SageMaker using the Hugging Face LLM Inference DLC.

EasyLLM is an open-source project that provides helpful tools and methods for working with large language models (LLMs).

This blog post is an extended guide on instruction-tuning Llama 2 from Meta AI

All Resources for LLaMA 2, How to test, train, and deploy it.

Learn how to train LLaMa 2 using QLoRA Hugging Face Transformers on Amazon SageMaker

Learn how to train LLMs using QLoRA on Amazon SageMaker

Learn how to deploy LLMs using Hugging Face Inference Endpoints

Learn how to deploy LLMs into VPCs from S3 with Amazon SageMaker using the new Hugging Face LLM Inference DLC.

Learn how to deploy Falcon 40B to Amazon SageMaker using the new Hugging Face LLM Inference DLC.

Learn how to deploy the open-source LLMs, like BLOOM to Amazon SageMaker for inference using the new Hugging Face LLM Inference Container.

IGEL (Instruction-based German Language Model) is an LLM designed for German language understanding tasks, including sentiment analysis, language translation, and question answering.