This lesson will cover:
- Exploring the different Mistral Models
- Understanding the use-cases and scenarios for each model
- Code samples show the unique features of each model.
In this lesson, we will explore 3 different Mistral models: Mistral Large, Mistral Small and Mistral Nemo.
Each of these models are available free on the Github Model marketplace. The code in this notebook will be using this models to run the code. Here are more details on using Github Models to prototype with AI models.
Mistral Large 2 is currently the flagship model from Mistral and is designed for enterprise use.
The model is an upgrade to the original Mistral Large by offering
- Larger Context Window - 128k vs 32k
- Better performance on Math and Coding Tasks - 76.9% average accuracy vs 60.4%
- Increased multilingual performance - languages include: English, French, German, Spanish, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic, and Hindi.
With these features, Mistral Large excels at
- Retrieval Augmented Generation (RAG) - due to the larger context window
- Function Calling - this model has native function calling which allows integration with external tools and APIs. These calls can be made both in parallel or one after another in a sequential order.
- Code Generation - this model excels on Python, Java, TypeScript and C++ generation.
In this example, we are using Mistral Large 2 to run a RAG pattern over a text document. The question is written in Korean and asks about the author's activities before college.
It uses Cohere Embeddings Model to create embeddings of the text document as well as the question. For this sample, it uses the faiss Python package as a vector store.
The prompt sent to the Mistral model includes both the questions and the retrieved chunks that are similar to the question. The Model then provides a natural language response.
pip install faiss-cpuimport requests
import numpy as np
import faiss
import os
from azure.ai.inference import ChatCompletionsClient
from azure.ai.inference.models import SystemMessage, UserMessage
from azure.core.credentials import AzureKeyCredential
from azure.ai.inference import EmbeddingsClient
endpoint = "https://models.inference.ai.azure.com"
model_name = "Mistral-large"
token = os.environ["GITHUB_TOKEN"]
client = ChatCompletionsClient(
endpoint=endpoint,
credential=AzureKeyCredential(token),
)
response = requests.get('https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt')
text = response.text
chunk_size = 2048
chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
len(chunks)
embed_model_name = "cohere-embed-v3-multilingual"
embed_client = EmbeddingsClient(
endpoint=endpoint,
credential=AzureKeyCredential(token)
)
embed_response = embed_client.embed(
input=chunks,
model=embed_model_name
)
text_embeddings = []
for item in embed_response.data:
length = len(item.embedding)
text_embeddings.append(item.embedding)
text_embeddings = np.array(text_embeddings)
d = text_embeddings.shape[1]
index = faiss.IndexFlatL2(d)
index.add(text_embeddings)
question = "저자가 대학에 오기 전에 주로 했던 두 가지 일은 무엇이었나요?？"
question_embedding = embed_client.embed(
input=[question],
model=embed_model_name
)
question_embeddings = np.array(question_embedding.data[0].embedding)
D, I = index.search(question_embeddings.reshape(1, -1), k=2) # distance, index
retrieved_chunks = [chunks[i] for i in I.tolist()[0]]
prompt = f"""
Context information is below.
---------------------
{retrieved_chunks}
---------------------
Given the context information and not prior knowledge, answer the query.
Query: {question}
Answer:
"""
chat_response = client.complete(
messages=[
SystemMessage(content="You are a helpful assistant."),
UserMessage(content=prompt),
],
temperature=1.0,
top_p=1.0,
max_tokens=1000,
model=model_name
)
print(chat_response.choices[0].message.content)Mistral Small is another model in the Mistral family of models under the premier/enterprise category. As the name implies, this model is a Small Language Model (SLM). The advantages of using Mistral Small are that it is:
- Cost Saving compared to Mistral LLMs like Mistral Large and NeMo - 80% price drop
- Low latency - faster response compared to Mistral's LLMs
- Flexible - can be deployed across different environments with less restrictions on required resources.
Mistral Small is great for:
- Text based tasks such as summarization, sentiment analysis and translation.
- Applications where frequent requests are made due to its cost effectiveness
- Low latency code tasks like review and code suggestions
To show differences in latency between Mistral Small and Large, run the below cells.
You should see a difference in response times between 3-5 seconds. Also not the response lengths and style over the smae prompt.
import os
endpoint = "https://models.inference.ai.azure.com"
model_name = "Mistral-small"
token = os.environ["GITHUB_TOKEN"]
client = ChatCompletionsClient(
endpoint=endpoint,
credential=AzureKeyCredential(token),
)
response = client.complete(
messages=[
SystemMessage(content="You are a helpful coding assistant."),
UserMessage(content="Can you write a Python function to the fizz buzz test?"),
],
temperature=1.0,
top_p=1.0,
max_tokens=1000,
model=model_name
)
print(response.choices[0].message.content)import os
from azure.ai.inference import ChatCompletionsClient
from azure.ai.inference.models import SystemMessage, UserMessage
from azure.core.credentials import AzureKeyCredential
endpoint = "https://models.inference.ai.azure.com"
model_name = "Mistral-large"
token = os.environ["GITHUB_TOKEN"]
client = ChatCompletionsClient(
endpoint=endpoint,
credential=AzureKeyCredential(token),
)
response = client.complete(
messages=[
SystemMessage(content="You are a helpful coding assistant."),
UserMessage(content="Can you write a Python function to the fizz buzz test?"),
],
temperature=1.0,
top_p=1.0,
max_tokens=1000,
model=model_name
)
print(response.choices[0].message.content)Compared to the other two models discussed in this lesson, Mistral NeMo is the only free model with an Apache2 License.
It is viewed as an upgrade to the earlier open source LLM from Mistral, Mistral 7B.
Some other feature of the NeMo model are:
-
More efficient tokenization: This model using the Tekken tokenizer over the more commonly used tiktoken. This allows for better performance over more languages and code.
-
Finetuning: The base model is available for finetuning. This allows for more flexibility for use-cases where finetuning may be needed.
-
Native Function Calling - Like Mistral Large, this model has been trained on function calling. This makes it unique as being one of the first open source models to do so.
In this sample, we will look at how Mistral NeMo handles tokenization compared to Mistral Large.
Both samples take the same prompt but you shoud see that NeMo returns back less tokens vs Mistral Large.
pip install mistral-common# Import needed packages:
from mistral_common.protocol.instruct.messages import (
UserMessage,
)
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.protocol.instruct.tool_calls import (
Function,
Tool,
)
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
# Load Mistral tokenizer
model_name = "open-mistral-nemo "
tokenizer = MistralTokenizer.from_model(model_name)
# Tokenize a list of messages
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
tools=[
Tool(
function=Function(
name="get_current_weather",
description="Get the current weather",
parameters={
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the users location.",
},
},
"required": ["location", "format"],
},
)
)
],
messages=[
UserMessage(content="What's the weather like today in Paris"),
],
model=model_name,
)
)
tokens, text = tokenized.tokens, tokenized.text
# Count the number of tokens
print(len(tokens))# Import needed packages:
from mistral_common.protocol.instruct.messages import (
UserMessage,
)
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.protocol.instruct.tool_calls import (
Function,
Tool,
)
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
# Load Mistral tokenizer
model_name = "mistral-large-latest"
tokenizer = MistralTokenizer.from_model(model_name)
# Tokenize a list of messages
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
tools=[
Tool(
function=Function(
name="get_current_weather",
description="Get the current weather",
parameters={
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the users location.",
},
},
"required": ["location", "format"],
},
)
)
],
messages=[
UserMessage(content="What's the weather like today in Paris"),
],
model=model_name,
)
)
tokens, text = tokenized.tokens, tokenized.text
# Count the number of tokens
print(len(tokens))After completing this lesson, check out our Generative AI Learning collection to continue leveling up your Generative AI knowledge!