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.
Discover models
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).
Deploy models
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:
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id=”huggingface-llm-mistral-7b-instruct”)
predictor = model.deploy()
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:
payload = {“inputs”: “<s>[INST] Hello! [/INST]”}
predictor.predict(payload)
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_INPUT_LENGTH and MAX_TOTAL_TOKENS to 8191 and 8192, respectively. You can view the full list by inspecting your model object:
print(model.env)
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_TOTAL_TOKENS or MAX_CONCURRENT_REQUESTS prior to deployment to provide the best performance for your workload:
model.env[“MAX_CONCURRENT_REQUESTS”] = “4”
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.
.
throughput (tokens/s)
concurrent users
1
2
4
8
16
32
64
128
model
instance type
input words
output tokens
.
mistral-7b-instruct
ml.g5.2xlarge
128
128
30
54
89
166
287
499
793
1030
512
128
29
50
80
140
210
315
383
458
6400
128
17
25
30
35
—
—
—
—
.
p50 latency (ms/token)
concurrent users
1
2
4
8
16
32
64
128
model
instance type
input words
output tokens
.
mistral-7b-instruct
ml.g5.2xlarge
128
128
32
33
34
36
41
46
59
88
512
128
34
36
39
43
54
71
112
213
6400
128
57
71
98
154
—
—
—
—
Inference and example prompts
Mistral 7B
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:
> Input
Tweet: “I get sad when my phone battery dies.”
Sentiment: Negative
###
Tweet: “My day has been :+1:”
Sentiment: Positive
###
Tweet: “This is the link to the article”
Sentiment: Neutral
###
Tweet: “This new music video was incredibile”
Sentiment:
> Output
Positive
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:
<s>[INST] {user_prompt} [/INST]
A multi-turn prompt would look like the following:
<s>[INST] {user_prompt_1} [/INST] {assistant_response_1} </s><s>[INST] {user_prompt_1} [/INST]
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.
Knowledge retrieval
The following is an example of knowledge retrieval:
> Input
<s>[INST] Which country has the most natural lakes? Answer with only the country name. [/INST]
> Output
1. Canada
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:
> Input
<s>[INST] A rat is a rodent, the most common mammal in the world. Rattus norvegicus is one of the approximately four hundred different kinds of rodents, and it is known by many names, each of which describes a trait or a perceived trait or sometimes a habitat: the earth rat, the roving rat, the barn rat, the fi eld rat, the migratory rat, the house rat, the sewer rat, the water rat, the wharf rat, the alley rat, the gray rat, the brown rat, and the common rat. The average brown rat is large and stocky; it grows to be approximately sixteen inches long from its nose to its tail—the size of a large adult human male’s foot—and weighs about a pound, though brown rats have been measured by scientists and exterminators at twenty inches and up to two pounds. The brown rat is sometimes confused with the black rat, or Rattus rattus, which is smaller and once inhabited New York City and all of the cities of America but, since Rattus norvegicus pushed it out, is now relegated to a minor role. (The two species still survive alongside each other in some Southern coastal cities and on the West Coast, in places like Los Angeles, for example, where the black rat lives in attics and palm trees.) The black rat is always a very dark gray, almost black, and the brown rat is gray or brown, with a belly that can be light gray, yellow, or even a pure-seeming white. One spring, beneath the Brooklyn Bridge, I saw a red-haired brown rat that had been run over by a car. Both pet rats and laboratory rats are Rattus norvegicus, but they are not wild and therefore, I would emphasize, not the subject of this book. Sometimes pet rats are called fancy rats. But if anyone has picked up this book to learn about fancy rats, then they should put this book down right away; none of the rats mentioned herein are at all fancy.
Rats are nocturnal, and out in the night the brown rat’s eyes are small and black and shiny; when a fl ashlight shines into them in the dark, the eyes of a rat light up like the eyes of a deer. Though it forages* in darkness, the brown rat has poor eyesight. It makes up for this with, fi rst of all, an excellent sense of smell. . . . They have an excellent sense of taste, detecting the most minute amounts of poison, down to one part per million. A brown rat has strong feet, the two front paws each equipped with four clawlike nails, the rear paws even longer and stronger. It can run and climb with squirrel-like agility. It is an excellent swimmer, surviving in rivers and bays, in sewer streams and toilet bowls.
The brown rat’s teeth are yellow, the front two incisors being especially long and sharp, like buckteeth. When the brown rat bites, its front two teeth spread apart. When it gnaws, a fl ap of skin plugs the space behind its incisors. Hence, when the rat gnaws on indigestible materials—concrete or steel, for example—the shavings don’t go down the rat’s throat and kill it. Its incisors grow at a rate of fi ve inches per year. Rats always gnaw, and no one is certain why—there are few modern rat studies. It is sometimes erroneously stated that the rat gnaws solely to limit the length of its incisors, which would otherwise grow out of its head, but this is not the case: the incisors wear down naturally. In terms of hardness, the brown rat’s teeth are stronger than aluminum, copper, lead, and iron. They are comparable to steel. With the alligator-like structure of their jaws, rats can exert a biting pressure of up to seven thousand pounds per square inch. Rats, like mice, seem to be attracted to wires—to utility wires, computer wires, wires in vehicles, in addition to gas and water pipes. One rat expert theorizes that wires may be attractive to rats because of their resemblance to vines and the stalks of plants; cables are the vines of the city. By one estimate, 26 percent of all electric-cable breaks and 18 percent of all phone-cable disruptions are caused by rats. According to one study, as many as 25 percent of all fi res of unknown origin are rat-caused. Rats chew electrical cables. Sitting in a nest of tattered rags and newspapers, in the fl oorboards of an old tenement, a rat gnaws the head of a match—the lightning in the city forest.
When it is not gnawing or feeding on trash, the brown rat digs. Anywhere there is dirt in a city, brown rats are likely to be digging—in parks, in fl owerbeds, in little dirt-poor backyards. They dig holes to enter buildings and to make nests. Rat nests can be in the floorboards of apartments, in the waste-stuffed corners of subway stations, in sewers, or beneath old furniture in basements. “Cluttered and unkempt alleyways in cities provide ideal rat habitat, especially those alleyways associated with food-serving establishments,” writes Robert Corrigan in Rodent Control, a pest control manual. “Alley rats can forage safely within the shadows created by the alleyway, as well as quickly retreat to the safety of cover in these narrow channels.” Often, rats burrow under concrete sidewalk slabs. Entrance to a typical under-the-sidewalk rat’s nest is gained through a two-inch-wide hole—their skeletons collapse and they can squeeze into a hole as small as three quarters of an inch wide, the average width of their skull. This tunnel then travels about a foot down to where it widens into a nest or den. The den is lined with soft debris, often shredded plastic garbage or shopping bags, but sometimes even grasses or plants; some rat nests have been found stuffed with the gnawed shavings of the wood-based, spring-loaded snap traps that are used in attempts to kill them. The back of the den then narrows into a long tunnel that opens up on another hole back on the street. This second hole is called a bolt hole; it is an emergency exit. A bolt hole is typically covered lightly with dirt or trash—camoufl age. Sometimes there are networks of burrows, which can stretch beneath a few concrete squares on a sidewalk, or a number of backyards, or even an entire city block—when Rattus norvegicus fi rst came to Selkirk, England, in 1776, there were so many burrows that people feared the town might sink. Rats can also nest in basements, sewers, manholes, abandoned pipes of any kind, fl oorboards, or any hole or depression. “Often,” Robert Corrigan writes, “‘city rats’ will live unbeknownst to people right beneath their feet.”
Rats also inhabit subways, as most people in New York City and any city with a subway system are well aware. Every once in a while, there are reports of rats boarding trains, but for the most part rats stay on the tracks—subway workers I have talked to refer to rats as “track rabbits.” People tend to think that the subways are fi lled with rats, but in fact rats are not everywhere in the system; they live in the subways according to the supply of discarded human food and sewer leaks. Sometimes, rats use the subway purely for nesting purposes; they fi nd ways through the walls of the subway stations leading from the tracks to the restaurants and stores on the street—the vibrations of subway trains tend to create rat-size cracks and holes. Many subway rats tend to live near stations that are themselves near fast-food restaurants. At the various subway stations near Herald Square, for example, people come down from the streets and throw the food that they have not eaten onto the tracks, along with newspapers and soda bottles and, I have noticed, thousands of nolonger-charged AA batteries, waiting to leak acid. The rats eat freely from the waste and sit at the side of the little streams of creamy brown sewery water that fl ows between the rails. They sip the water the way rats do, either with their front paws or by scooping it up with their incisors.
What is the most likely reason the author states, “cables are the vines of the city”? [/INST]
> Output
1. The author states that rats are attracted to wires, such as utility wires, computer wires, and vehicle wires, which may be due to their resemblance to vines and the stalks of plants.
Mathematics and reasoning
The Mistral models also report strengths in mathematics accuracy. Mistral can provide comprehension such as the following math logic:
> Input
<s>[INST] I bought an ice cream for 6 kids. Each cone was $1.25 and I paid with a $10 bill. How many dollars did I get back? Explain first before answering. [/INST]
> Output
1. First, let’s figure out how much the ice cream cost in total. Since each cone was $1.25 and there were 6 kids, the total cost would be:
Total cost = Number of cones * Cost per cone
Total cost = 6 * $1.25
Total cost = $7.50
2. Next, we’ll subtract the total cost from the amount paid ($10) to find out how much money was returned:
Money returned = Amount paid – Total cost
Money returned = $10 – $7.50
Money returned = $2.50
So, you got back $2.50 after buying the ice cream for the 6 kids.
Coding
The following is an example of a coding prompt:
> Input
<s>[INST] In Bash, how do I list all text files in the current directory (excluding subdirectories) that have been modified in the last month? [/INST]
> Output
1. Use the `find` command to search for text files in the current directory (excluding subdirectories) that have been modified in the last month. The `-type f` option specifies that we only want to search for files, not directories. The `-name “*.txt”` option specifies that we only want to search for files with the `.txt` extension. The `-mtime -30` option specifies that we only want to search for files that have been modified in the last 30 days (1 month).
“`bash
find . -type f -name “*.txt” -mtime -30
“`
This command will list all text files in the current directory (excluding subdirectories) that have been modified in the last month.
Clean up
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:
predictor.delete_model()
predictor.delete_endpoint()
Conclusion
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.
Resources
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.
