inference
Artificial Intelligence
High-Speed Inference with llama.cpp and Vicuna on CPU Arrange llama.cpp in your computer Prompting Vicuna with llama.cpp llama.cpp’s chat mode Using other models with llama.cpp: An Example with...
You don’t need a GPU for fast inferenceFor inference with large language models, we might imagine that we want a really big GPU or that it will probably’t run on consumer hardware. This is...
Artificial Intelligence
Boosting PyTorch Inference on CPU: From Post-Training Quantization to Multithreading Problem Statement: Deep Learning Inference under Limited Time and Computation Constraints Approaching Deep Learning Inference on...
For an in-depth explanation of post-training quantization and a comparison of ONNX Runtime and OpenVINO, I like to recommend this text:This section will specifically have a look at two popular techniques of post-training quantization:ONNX...
Artificial Intelligence
QLoRa: Wonderful-Tune a Large Language Model on Your GPU QLoRa: Quantized LLMs with Low-Rank Adapters Wonderful-tuning a GPT model with QLoRa GPT Inference with QLoRa Conclusion
Most large language models (LLM) are too big to be fine-tuned on consumer hardware. As an example, to fine-tune a 65 billion parameters model we'd like greater than 780 Gb of GPU memory. That...
Artificial Intelligence
OpenAI, ChatGPT unveils ways to enhance hallucination problems
OpenAI has unveiled a recent method to enhance the hallucination problem of 'ChatGPT' with a human-like pondering approach.
In line with CNBC, in a paper published on the thirty first (local time), OpenAI hallucinates artificial...
Artificial Intelligence
The Power of Bayesian Causal Inference: A Comparative Evaluation of Libraries to Reveal Hidden Causality in Your Dataset.
Library 1: Bnlearn for Python.Bnlearn is a Python package that's suited to creating and analyzing Bayesian Networks, for discrete, mixed, and continuous data sets . It's designed to be ease-of-use and comprises the most-wanted...
Artificial Intelligence
Create Your Own Metropolis-Hastings Markov Chain Monte Carlo Algorithm for Bayesian Inference (With Python) Level Up Coding
In today’s recreational coding exercise, we learn the way to fit model parameters to data (with error bars) and acquire the more than likely distribution of modeling parameters that best explain the info, called...
Artificial Intelligence
Quantizing OpenAI’s Whisper with the Huggingface Optimum Library → >30% Faster Inference, 64% Lower Memory tl;dr Introduction Step 1: Install requirements Step 2: Quantize the model Step 3: Compare...
Save 30% inference time and 64% memory when transcribing audio with OpenAI’s Whisper model by running the below code.Get in contact with us for those who are inquisitive about learning more.With all of the...
Artificial Intelligence
Traceability & Reproducibility Our motivation: Things can go incorrect Our solution: Traceability by design Solution design for real-time inference model: Traceability on real-time inference model: Reproducibility: Roll-back
Within the context of MLOps, traceability is the flexibility to trace the history of knowledge, code for training and prediction, model artifacts, environment utilized in development and deployment. Reproducibility is the flexibility to breed...
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