Releases: mistralai/mistral-common
Releases · mistralai/mistral-common
Patch Release v1.5.1
What's Changed
- [From model] Make from model much stricter by @patrickvonplaten in #65
Full Changelog: v1.5.0...v1.5.1
Contributors
patrickvonplaten
Assets 2
1.5.0 - Mistral Tokenizer v7 (new System Prompt + Fn calling)
Mistral's newest tokenizer has two major improvements:
System prompt
Similar to other tokenization schemes the system prompt is now treated as a "normal" message encapsulated by [SYSTEM_PROMPT] ...[\SYSTEM_PROMPT]
E.g.
from mistral_common.protocol.instruct.messages import (
UserMessage,
SystemMessage,
AssistantMessage,
)
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
# Load Mistral tokenizer
tokenizer = MistralTokenizer.v7()
# Tokenize a list of messages
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
messages=[
SystemMessage(content="You are a funny AI assistant. Always make jokes."),
UserMessage(content="What's the weather like today in Paris"),
],
model="joker",
)
)
tokens, text = tokenized.tokens, tokenized.text
print(text)
# <s>[SYSTEM_PROMPT]▁You▁are▁a▁funny▁AI▁assistant.▁Always▁make▁jokes.[/SYSTEM_PROMPT][INST]▁What's▁the▁weather▁like▁today▁in▁Paris[/INST]Improve function calling
A new [TOOL_CONTENT] is added if trained with correctly should improve the accuracy of function calling.
from mistral_common.protocol.instruct.messages import (
UserMessage,
SystemMessage,
AssistantMessage,
ToolMessage
)
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
tokenizer = MistralTokenizer.v7()
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"),
AssistantMessage(content="", tool_calls=[
{
"id": "bbc5b7ede",
"type": "function",
"function": {
"name": "weather",
"arguments": '{"location": "Paris", "format": "celsius"}',
},
}
]),
ToolMessage(content="24 degrees celsius", tool_call_id="bbc5b7ede"),
],
model="joker",
)
)
tokens, text = tokenized.tokens, tokenized.text
# Count the number of tokens
print(text)
# <s>[AVAILABLE_TOOLS]▁[{"type":▁"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"]}}}][/AVAILABLE_TOOLS][INST]▁What\'s▁the▁weather▁like▁today▁in▁Paris[/INST][TOOL_CALLS]▁[{"name":▁"weather",▁"arguments":▁{"location":▁"Paris",▁"format":▁"celsius"},▁"id":▁"bbc5b7ede"}]</s>[TOOL_RESULTS]▁bbc5b7ede[TOOL_CONTENT]▁24▁degrees▁celsius[/TOOL_RESULTS]'Patch release - v1.4.4
Make sure broken user envs of cv2 (which sadly happens more often than not) don't impede users from using text-only models.
What's Changed
- [cv2] Make sure broken environments don't lead to errors by @patrickvonplaten in #58
Full Changelog: v1.4.3...v1.4.4
Contributors
patrickvonplaten
Assets 2
Patch release v1.4.3 - Make cv2 install optional
As per discussion: vllm-project/vllm#8650 make cv2 optional.
What's Changed
- [Error Message] Improve error message by @patrickvonplaten in #54
- Make cv2 optional by @patrickvonplaten in #56
Full Changelog: v1.4.2...v1.4.3
Contributors
patrickvonplaten
Assets 2
Patch release v1.4.2
Make sure to send user agent for downloading pictures that require a user agent. E.g.:
from mistral_common.protocol.instruct.messages import (
UserMessage,
TextChunk,
ImageURLChunk,
ImageChunk,
)
from PIL import Image
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
tokenizer = MistralTokenizer.from_model("pixtral")
url_dog = "https://picsum.photos/id/237/200/300"
url_mountain = "https://picsum.photos/seed/picsum/200/300"
url1 = "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg"
url2 = "https://upload.wikimedia.org/wikipedia/commons/7/77/002_The_lion_king_Snyggve_in_the_Serengeti_National_Park_Photo_by_Giles_Laurent.jpg"
# tokenize image urls and text
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text="Can this animal"),
ImageURLChunk(image_url=url1),
TextChunk(text="live here?"),
ImageURLChunk(image_url=url2),
]
)
],
model="pixtral",
)
)
tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images
# Count the number of tokens
print("# tokens", len(tokens))
print("# images", len(images))What's Changed
- typo: Correct "recieved" to "received" in MistralRequestValidator by @CharlesCNorton in #21
- Add headers to download images by @ywang96 in #51
- [Tests] Fix expected value by @patrickvonplaten in #50
- 1.4.2 release by @patrickvonplaten in #53
New Contributors
- @CharlesCNorton made their first contribution in #21
- @ywang96 made their first contribution in #51
Full Changelog: v1.4.1...v1.4.2
Contributors
patrickvonplaten, CharlesCNorton, and ywang96
Assets 2
Patch release v1.4.1 - Use cv2 resize instead of PIL
cv2 resize gives significantly better results when running pixtral in inference as compared to PIL hence we're making a patch release to resize images using cv2 as shown here: bae45b2
v1.4.0 - Mistral common goes 🖼️
Pixtral is out!
Mistral common has image support! You can now pass images and URLs alongside text into the user message.
pip install --upgrade mistral_common
Images
You can encode images as follows
from mistral_common.protocol.instruct.messages import (
UserMessage,
TextChunk,
ImageURLChunk,
ImageChunk,
)
from PIL import Image
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
tokenizer = MistralTokenizer.from_model("pixtral")
image = Image.new('RGB', (64, 64))
# tokenize images and text
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text="Describe this image"),
ImageChunk(image=image),
]
)
],
model="pixtral",
)
)
tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images
# Count the number of tokens
print("# tokens", len(tokens))
print("# images", len(images))Image URLs
You can pass image url which will be automatically downloaded
url_dog = "https://picsum.photos/id/237/200/300"
url_mountain = "https://picsum.photos/seed/picsum/200/300"
# tokenize image urls and text
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text="Can this animal"),
ImageURLChunk(image_url=url_dog),
TextChunk(text="live here?"),
ImageURLChunk(image_url=url_mountain),
]
)
],
model="pixtral",
)
)
tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images
# Count the number of tokens
print("# tokens", len(tokens))
print("# images", len(images))ImageData
You can also pass image encoded as base64
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text="What is this?"),
ImageURLChunk(image_url="data:image/jpeg;base64,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release 1.3.4 - Loosen pydantic requirement
In this patch release the pydantic requirement is loosened to be <= 3.0.0
as noticed in multiple issues, e.g.:
Tekkenizer
Tekkenizer
The new Tekkenizer class is based on Open AI's tiktoken and supports the new Mistral-Nemo model.
Tekkenizer always makes use of version 3 or higher.
Examples:
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
tokenizer = MistralTokenizer.v3(is_tekken=True)
tokenizer = MistralTokenizer.from_model("...")Function calling (just like before)
# 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 = "..."
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))What's Changed
- v1.3.0 by @patrickvonplaten in #27
Full Changelog: v1.3.0...v1.3.1
Contributors
patrickvonplaten
Assets 2
Patch release: Fix FIM tokenizer
As noticed here: https://huggingface.co/mistralai/Codestral-22B-v0.1/discussions/10
The wrong tokenizer was used for FIM. This patch release fixes that so that the following works correctly:
from mistral_common.tokens.tokenizers.base import FIMRequest
from mistral_common_private.tokens.tokenizers.mistral import MistralTokenizer
tokenizer = MistralTokenizer.v3()
tokenized = tokenizer.encode_fim(FIMRequest(prompt="def f(", suffix="return a + b"))
assert tokenized.text == "<s>[SUFFIX]return▁a▁+▁b[PREFIX]▁def▁f("