A comparison of frameworks, models, and costs for deploying Llama models locally and privately.
- Four tools were analyzed: HuggingFace, vLLM, Ollama, and llama.cpp.
- HuggingFace has a wide range of models but struggles with quantized models.
- vLLM is experimental and lacks full support for quantized models.
- Ollama is user-friendly but has some customization limitations.
- llama.cpp is preferred for its performance and customization options.
- The analysis focused on llama.cpp and Ollama, comparing speed and power consumption across different quantizations.
- Four tools were analyzed: HuggingFace, vLLM, Ollama, and llama.cpp.
- HuggingFace has a wide range of models but struggles with quantized models.
- vLLM is experimental and lacks full support for quantized models.
- Ollama is user-friendly but has some customization limitations.
- llama.cpp is preferred for its performance and customization options.
- The analysis focused on llama.cpp and Ollama, comparing speed and power consumption across different quantizations.
Microsoft has released the OmniParser model on HuggingFace, a vision-based tool designed to parse UI screenshots into structured elements, enhancing intelligent GUI automation across platforms without relying on additional contextual data.
Ollama now supports HuggingFace GGUF models, making it easier for users to run AI models locally without internet. The GGUF format allows for the use of AI models on modest-sized consumer hardware.
This paper analyzes the performance of 20 large language models (LLMs) using two inference libraries: vLLM and HuggingFace Pipelines. The study investigates how hyperparameters influence inference performance and reveals that throughput landscapes are irregular, highlighting the importance of hyperparameter optimization.
HuggingFace has released FineWeb, a new large-scale dataset consisting of 15 trillion tokens and 44TB of disk space designed for pretraining large language models (LLMs). The dataset, which leverages data from CommonCrawl, undergoes rigorous deduplication and quality filtering processes, making it a valuable tool for researchers.
This article explains how to use the Sentence Transformers library to finetune and train embedding models for a variety of applications, such as retrieval augmented generation, semantic search, and semantic textual similarity. It covers the training components, dataset format, loss function, training arguments, evaluators, and trainer.
This model was built using a new Smaug recipe for improving performance on real world multi-turn conversations applied to meta-llama/Meta-Llama-3-70B-Instruct.
The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below).
The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below).
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))"
