Remove APIs Remove Calibration Remove Examples Remove Groups
article thumbnail

Boost inference performance for Mixtral and Llama 2 models with new Amazon SageMaker containers

AWS Machine Learning

Be mindful that LLM token probabilities are generally overconfident without calibration. Transformers-NeuronX backend The updated release of NeuronX included in the LMI NeuronX DLC now supports models that feature the grouped-query attention mechanism, such as Mistral-7B and LLama2-70B.

article thumbnail

Model management for LoRA fine-tuned models using Llama2 and Amazon SageMaker

AWS Machine Learning

Additionally, optimizing the training process and calibrating the parameters can be a complex and iterative process, requiring expertise and careful experimentation. Working with FMs on SageMaker Model Registry In this post, we walk through an end-to-end example of fine-tuning the Llama2 large language model (LLM) using the QLoRA method.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Run secure processing jobs using PySpark in Amazon SageMaker Pipelines

AWS Machine Learning

In our example, we create a SageMaker pipeline running a single processing step. SageMaker Processing library SageMaker Processing can run with specific frameworks (for example, SKlearnProcessor, PySparkProcessor, or Hugging Face). We provide examples of this configuration using the SageMaker SDK in the next section.

article thumbnail

Detect fraudulent transactions using machine learning with Amazon SageMaker

AWS Machine Learning

For the sample dataset, we use the public, anonymized credit card transactions dataset that was originally released as part of a research collaboration of Worldline and the Machine Learning Group of ULB (Université Libre de Bruxelles). With each data example, RCF associates an anomaly score. Prerequisites. Launch the solution.

APIs 66
article thumbnail

Face-off Probability, part of NHL Edge IQ: Predicting face-off winners in real time during televised games

AWS Machine Learning

We explored nearest neighbors, decision trees, neural networks, and also collaborative filtering in terms of algorithms, while trying different sampling strategies (filtering, random, stratified, and time-based sampling) and evaluated performance on Area Under the Curve (AUC) and calibration distribution along with Brier score loss.

article thumbnail

Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services

AWS Machine Learning

Additionally, we provide code example in this GitHub repository to enable the users to conduct parallel multi-model evaluation at scale, using examples such as Llama2-7b-f, Falcon-7b, and fine-tuned Llama2-7b models. Evaluating these models allows continuous model improvement, calibration and debugging.

article thumbnail

Brand Move Roundup – May 18, 2020

C Space

Giphy’s tools are already integrated with many Facebook competitors, including Twitter, Snapchat, Slack, Reddit, TikTok and Bumble , and both companies have said that Giphy’s outside partners will continue to have the same access to its library and API. This week S chenck Process , a German manufacturing group, added back €5.4m