Releases: mistralai/mistral-common
Releases · mistralai/mistral-common
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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...Patch 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("mistral-common v1.2.0
Fill-in-the-middle (FIM) with [SUFFIX] and [PREFIX] logic is added to allow building code completion workflows such as:
from mistral_inference.model import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.instruct.request import FIMRequest
tokenizer = MistralTokenizer.v3()
model = Transformer.from_folder("~/codestral-22B-240529")
prefix = """def add("""
suffix = """ return sum"""
request = FIMRequest(prompt=prefix, suffix=suffix)
tokens = tokenizer.encode_fim(request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.decode(out_tokens[0])
middle = result.split(suffix)[0].strip()
print(middle)mistral-common v1.1.0
- Adds improved function calling validator
- Adds improved fine-tuning assistant message