Today, we are excited to announce that the Mistral 7B foundation models, developed by Mistral AI, are available for customers through Amazon SageMaker JumpStart to deploy with one click for running inference. With 7 billion parameters, Mistral 7B can be easily customized and quickly deployed. You can try out this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms and models so you can quickly get started with ML. In this post, we walk through how to discover and deploy the Mistral 7B model.
What is Mistral 7B
Mistral 7B is a foundation model developed by Mistral AI, supporting English text and code generation abilities. It supports a variety of use cases, such as text summarization, classification, text completion, and code completion. To demonstrate the easy customizability of the model, Mistral AI has also released a Mistral 7B Instruct model for chat use cases, fine-tuned using a variety of publicly available conversation datasets.
Mistral 7B is a transformer model and uses grouped-query attention and sliding-window attention to achieve faster inference (low latency) and handle longer sequences. Group query attention is an architecture that combines multi-query and multi-head attention to achieve output quality close to multi-head attention and comparable speed to multi-query attention. Sliding-window attention uses the stacked layers of a transformer to attend in the past beyond the window size to increase context length. Mistral 7B has an 8,000-token context length, demonstrates low latency and high throughput, and has strong performance when compared to larger model alternatives, providing low memory requirements at a 7B model size. The model is made available under the permissive Apache 2.0 license, for use without restrictions.
What is SageMaker JumpStart
With SageMaker JumpStart, ML practitioners can choose from a growing list of best-performing foundation models. ML practitioners can deploy foundation models to dedicated Amazon SageMaker instances within a network isolated environment, and customize models using SageMaker for model training and deployment.
You can now discover and deploy Mistral 7B with a few clicks in Amazon SageMaker Studio or programmatically through the SageMaker Python SDK, enabling you to derive model performance and MLOps controls with SageMaker features such as Amazon SageMaker Pipelines, Amazon SageMaker Debugger, or container logs. The model is deployed in an AWS secure environment and under your VPC controls, helping ensure data security.
You can access Mistral 7B foundation models through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK. In this section, we go over how to discover the models in SageMaker Studio.
SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all ML development steps, from preparing data to building, training, and deploying your ML models. For more details on how to get started and set up SageMaker Studio, refer to Amazon SageMaker Studio.
In SageMaker Studio, you can access SageMaker JumpStart, which contains pre-trained models, notebooks, and prebuilt solutions, under Prebuilt and automated solutions.
From the SageMaker JumpStart landing page, you can browse for solutions, models, notebooks, and other resources. You can find Mistral 7B in the Foundation Models: Text Generation carousel.
You can also find other model variants by choosing Explore all Text Models or searching for “Mistral.”
You can choose the model card to view details about the model such as license, data used to train, and how to use. You will also find two buttons, Deploy and Open notebook, which will help you use the model (the following screenshot shows the Deploy option).
Deployment starts when you choose Deploy. Alternatively, you can deploy through the example notebook that shows up when you choose Open notebook. The example notebook provides end-to-end guidance on how to deploy the model for inference and clean up resources.
To deploy using notebook, we start by selecting the Mistral 7B model, specified by the
model_id. You can deploy any of the selected models on SageMaker with the following code:
This deploys the model on SageMaker with default configurations, including default instance type (ml.g5.2xlarge) and default VPC configurations. You can change these configurations by specifying non-default values in JumpStartModel. After it’s deployed, you can run inference against the deployed endpoint through the SageMaker predictor:
Optimizing the deployment configuration
Mistral models use Text Generation Inference (TGI version 1.1) model serving. When deploying models with the TGI deep learning container (DLC), you can configure a variety of launcher arguments via environment variables when deploying your endpoint. To support the 8,000-token context length of Mistral 7B models, SageMaker JumpStart has configured some of these parameters by default: we set
MAX_TOTAL_TOKENS to 8191 and 8192, respectively. You can view the full list by inspecting your model object:
By default, SageMaker JumpStart doesn’t clamp concurrent users via the environment variable
MAX_CONCURRENT_REQUESTS smaller than the TGI default value of 128. The reason is because some users may have typical workloads with small payload context lengths and want high concurrency. Note that the SageMaker TGI DLC supports multiple concurrent users through rolling batch. When deploying your endpoint for your application, you might consider whether you should clamp
MAX_CONCURRENT_REQUESTS prior to deployment to provide the best performance for your workload:
Here, we show how model performance might differ for your typical endpoint workload. In the following tables, you can observe that small-sized queries (128 input words and 128 output tokens) are quite performant under a large number of concurrent users, reaching token throughput on the order of 1,000 tokens per second. However, as the number of input words increases to 512 input words, the endpoint saturates its batching capacity—the number of concurrent requests allowed to be processed simultaneously—resulting in a throughput plateau and significant latency degradations starting around 16 concurrent users. Finally, when querying the endpoint with large input contexts (for example, 6,400 words) simultaneously by multiple concurrent users, this throughput plateau occurs relatively quickly, to the point where your SageMaker account will start encountering 60-second response timeout limits for your overloaded requests.
|model||instance type||input words||output tokens||.|
|.||p50 latency (ms/token)|
|model||instance type||input words||output tokens||.|
Inference and example prompts
You can interact with a base Mistral 7B model like any standard text generation model, where the model processes an input sequence and outputs predicted next words in the sequence. The following is a simple example with multi-shot learning, where the model is provided with several examples and the final example response is generated with contextual knowledge of these previous examples:
Mistral 7B instruct
The instruction-tuned version of Mistral accepts formatted instructions where conversation roles must start with a user prompt and alternate between user and assistant. A simple user prompt may look like the following:
A multi-turn prompt would look like the following:
This pattern repeats for however many turns are in the conversation.
In the following sections, we explore some examples using the Mistral 7B Instruct model.
The following is an example of knowledge retrieval:
Large context question answering
To demonstrate how to use this model to support large input context lengths, the following example embeds a passage, titled “Rats” by Robert Sullivan (reference), from the MCAS Grade 10 English Language Arts Reading Comprehension test into the input prompt instruction and asks the model a directed question about the text:
Mathematics and reasoning
The Mistral models also report strengths in mathematics accuracy. Mistral can provide comprehension such as the following math logic:
The following is an example of a coding prompt:
After you’re done running the notebook, make sure to delete all the resources that you created in the process so your billing is stopped. Use the following code:
In this post, we showed you how to get started with Mistral 7B in SageMaker Studio and deploy the model for inference. Because foundation models are pre-trained, they can help lower training and infrastructure costs and enable customization for your use case. Visit Amazon SageMaker JumpStart now to get started.
- SageMaker JumpStart documentation
- SageMaker JumpStart foundation models documentation
- SageMaker JumpStart product detail page
- SageMaker JumpStart model catalog
About the Authors
Dr. Kyle Ulrich is an Applied Scientist with the Amazon SageMaker JumpStart team. His research interests include scalable machine learning algorithms, computer vision, time series, Bayesian non-parametrics, and Gaussian processes. His PhD is from Duke University and he has published papers in NeurIPS, Cell, and Neuron.
Dr. Ashish Khetan is a Senior Applied Scientist with Amazon SageMaker JumpStart and helps develop machine learning algorithms. He got his PhD from University of Illinois Urbana-Champaign. He is an active researcher in machine learning and statistical inference, and has published many papers in NeurIPS, ICML, ICLR, JMLR, ACL, and EMNLP conferences.
Vivek Singh is a product manager with Amazon SageMaker JumpStart. He focuses on enabling customers to onboard SageMaker JumpStart to simplify and accelerate their ML journey to build generative AI applications.
Roy Allela is a Senior AI/ML Specialist Solutions Architect at AWS based in Munich, Germany. Roy helps AWS customers—from small startups to large enterprises—train and deploy large language models efficiently on AWS. Roy is passionate about computational optimization problems and improving the performance of AI workloads.