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Mistral 3

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Model ini disumbangkan ke Hugging Face Transformers pada 2025-03-18.

Mistral 3

Mistral 3 adalah model yang dioptimalkan untuk latensi dengan jumlah lapisan yang jauh lebih sedikit guna mengurangi waktu per langkah maju. Model ini menambahkan pemahaman visual dan mendukung panjang konteks hingga 128K token tanpa mengorbankan kinerja.

Anda dapat menemukan checkpoint asli Mistral 3 di bawah organisasi Mistral AI.

Model ini disumbangkan oleh cyrilvallez dan yonigozlan. Klik model Mistral3 di bilah sisi kanan untuk melihat lebih banyak contoh cara menerapkan Mistral3 ke berbagai tugas.

Setel use_kernels=True di from_pretrained() untuk mengganti lapisan yang didukung dengan kernel yang dioptimalkan dari Hub. Lihat Memuat kernel untuk mempelajari lebih lanjut.

Contoh di bawah ini menunjukkan cara menghasilkan teks untuk gambar dengan Pipeline dan kelas AutoModel.

Pipeline

AutoModel

Catatan

Mistral 3 mendukung generasi teks saja.

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText

model_checkpoint = ".mistralai/Mistral-Small-3.1-24B-Instruct-2503"
processor = AutoProcessor.from_pretrained(model_checkpoint)
model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map="auto")

SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": user_prompt},
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, return_tensors="pt").to(0)
generate_ids = model.generate(**inputs, max_new_tokens=50, do_sample=False)
decoded_output = processor.batch_decode(generate_ids[:, inputs["input_ids"].shape[1] :], skip_special_tokens=True)[0]

print(decoded_output)
"1. À plus tard!
 2. Salut, à plus!
 3. À toute!
 4. À la prochaine!
 5. Je me casse, à plus!

/_/\ ( o.o )

^ <

Mistral 3 menerima input gambar dan teks yang di-batch.

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText

model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
processor = AutoProcessor.from_pretrained(model_checkpoint)
model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map="auto")

messages = [
     [
         {
             "role": "user",
             "content": [
                 {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
                 {"type": "text", "text": "Write a haiku for this image"},
             ],
         },
     ],
     [
         {
             "role": "user",
             "content": [
                 {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
                 {"type": "text", "text": "Describe this image"},
             ],
         },
     ],
 ]


 inputs = processor.apply_chat_template(messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device)

 output = model.generate(**inputs, max_new_tokens=25)

 decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
 decoded_outputs
["Write a haiku for this imageCalm waters reflect\nWhispers of the forest's breath\nPeace on wooden path"
, "Describe this imageThe image depicts a vibrant street scene in what appears to be a Chinatown district. The focal point is a traditional Chinese"]

Mistral 3 juga mendukung input gambar dan teks yang di-batch dengan jumlah gambar yang berbeda untuk setiap teks. Contoh di bawah ini mengkuantisasi model dengan bitsandbytes.

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig

model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
processor = AutoProcessor.from_pretrained(model_checkpoint)
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
model = AutoModelForImageTextToText.from_pretrained(
     model_checkpoint, quantization_config=quantization_config
 device_map="auto")

messages = [
     [
         {
             "role": "user",
             "content": [
                 {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
                 {"type": "text", "text": "Write a haiku for this image"},
             ],
         },
     ],
     [
         {
             "role": "user",
             "content": [
                 {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/statue_of_liberty.jpg"},
                 {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/dreamstime_golden_gate_flowers.jpg"},
                 {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
             ],
         },
     ],
 ]

 inputs = processor.apply_chat_template(messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device)

 output = model.generate(**inputs, max_new_tokens=25)

 decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
 decoded_outputs
["Write a haiku for this imageSure, here is a haiku inspired by the image:\n\nCalm lake's wooden path\nSilent forest stands guard\n", "These images depict two different landmarks. Can you identify them? Certainly! The images depict two iconic landmarks:\n\n1. The first image shows the Statue of Liberty in New York City."]

Transformers

Mistral 3

Catatan

Mistral3Config

kelas transformers.Mistral3Config

MistralCommonBackend

kelas transformers.MistralCommonBackend

add_special_tokens

add_tokens

apply_chat_template

batch_decode

build_inputs_with_special_tokens

convert_added_tokens

create_token_type_ids_from_sequences

decode

encode

from_pretrained

get_chat_template

get_special_tokens_mask

get_vocab

num_special_tokens_to_add

prepare_for_model

save_chat_templates

save_pretrained

save_vocabulary

tokenize

truncate_sequences

Mistral3Model

kelas transformers.Mistral3Model

forward

get_image_features

get_placeholder_mask

Mistral3ForConditionalGeneration

kelas transformers.Mistral3ForConditionalGeneration

forward

get_image_features