mistralai/mistral-inference v1.4.0

v1.4.0: Pixtral 👀

Repository: mistralai/mistral-inference

Tag: v1.4.0

Published: 2024-09-13T13:09:47Z

Prerelease: no

Release notes: Pixtral

Mistral models can now 👀 !

pip install --upgrade mistral_inference # >= 1.4.0

Download:

from huggingface_hub import snapshot_download
from pathlib import Path

mistral_models_path = Path.home().joinpath('mistral_models', 'Pixtral')
mistral_models_path.mkdir(parents=True, exist_ok=True)

snapshot_download(repo_id="mistralai/Pixtral-12B-2409", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)

CLI example:

mistral-chat $HOME/mistral_models/Pixtral --instruct --max_tokens 256 --temperature 0.35

*E.g.* Try out something like:

Text prompt: What can you see on the following picture?
[You can input zero, one or more images now.]
Image path or url [Leave empty and press enter to finish image input]: https://picsum.photos/id/237/200/300
Image path or url [Leave empty and press enter to finish image input]:
I see a black dog lying on a wooden surface. The dog appears to be looking up, and its eyes are clearly visible.

Python:

1. Load the model

from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage, TextChunk, ImageURLChunk
from mistral_common.protocol.instruct.request import ChatCompletionRequest

tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
model = Transformer.from_folder(mistral_models_path)

2. Run:

url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
prompt = "Describe the image."

completion_request = ChatCompletionRequest(messages=[UserMessage(content=[ImageURLChunk(image_url=url), TextChunk(text=prompt)])])

encoded = tokenizer.encode_chat_completion(completion_request)

images = encoded.images
tokens = encoded.tokens

out_tokens, _ = generate([tokens], model, images=[images], max_tokens=256, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.decode(out_tokens[0])

print(result)