Impact Score: 5/10
Hugging Face Transformers: Mistral 3 (Based on v4.49.0)
Source available — automated, not human-reviewed
Component: transformers · Channel: stable. Automated analysis, not a compatibility certification. How this works.
Analysis updated: 2026-11-01.
⚡ Executive Summary (TL;DR)
Hugging Face Transformers v4.49.0-Mistral-3. Source highlight: A new model is added to transformers: Mistral 3.
Who Does This Impact? (Role-Based Analysis)
💻 Software Engineers & Devs
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📊 Product & Engineering Leads
Use the documented changes to assess relevance to your product; release frequency alone is not a quality measure.
⚙️ DevOps & Security
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📄 View Raw Release Notes (Original Developer Log)
A new model is added to transformers: Mistral 3.
It is added on top of the v4.49.0 release, and can be installed from the following tag: v4.49.0-Mistral-3.
In order to install this version, please install with the following command:
```
pip install git+https://github.com/huggingface/[email protected]
```
If fixes are needed, they will be applied to this release; this installation may therefore be considered as stable and improving.
# Mistral 3
The model is detailed in the following [blog post](https://mistral.ai/news/mistral-small-3-1).
The models are available on the Hub with the following tag: [`mistral3`](https://huggingface.co/models?other=mistral3)
## Overview
Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.
It is ideal for:
- Fast-response conversational agents.
- Low-latency function calling.
- Subject matter experts via fine-tuning.
- Local inference for hobbyists and organizations handling sensitive data.
- Programming and math reasoning.
- Long document understanding.
- Visual understanding.
This model was contributed by [cyrilvallez](https://huggingface.co/cyrilvallez) and [yonigozlan](https://huggingface.co/yonigozlan).
The original code can be found [here](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/pixtral.py) and [here](https://github.com/mistralai/mistral-common).
## Usage example
### Inference with Pipeline
Here is how you can use the `image-text-to-text` pipeline to perform inference with the `Mistral3` models in just a few lines of code:
```python
>>> from transformers import pipeline
>>> messages = [
... {
... "role": "user",
... "content": [
... {
... "type": "image",
... "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
... },
... {"type": "text", "text": "Describe this image."},
... ],
... },
... ]
>>> pipe = pipeline("image-text-to-text", model="mistralai/Mistral-Small-3.1-24B-Instruct-2503", torch_dtype=torch.bfloat16)
>>> outputs = pipe(text=messages, max_new_tokens=50, return_full_text=False)
>>> outputs[0]["generated_text"]
'The image depicts a vibrant and lush garden scene featuring a variety of wildflowers and plants. The central focus is on a large, pinkish-purple flower, likely a Greater Celandine (Chelidonium majus), with a'
```
### Inference on a single image
This example demonstrates how to perform inference on a single image with the Mistral3 models using chat templates.
```python
>>> from transformers import AutoProcessor, AutoModelForImageTextToText
>>> import torch
>>> torch_device = "cuda"
>>> model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
>>> messages = [
... {
... "role": "user",
... "content": [
... {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
... {"type": "text", "text": "Describe this image"},
... ],
... }
... ]
>>> inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)
>>> generate_ids = model.generate(**inputs, max_new_tokens=20)
>>> decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
>>> decoded_output
"The image depicts two cats lying on a pink blanket. The larger cat, which appears to be an"...
```
### Text-only generation
This example shows how to generate text using the Mistral3 model without providing any image input.
````python
>>> from transformers import AutoProcessor, AutoModelForImageTextToText
>>> import torch
>>> torch_device = "cuda"
>>> model_checkpoint = ".mistralai/Mistral-Small-3.1-24B-Instruct-2503"
>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
>>> 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, dtype=torch.float16)
>>> 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 )
> ^ <
```"
````
### Batched image and text inputs
Mistral3 models also support batched image and text inputs.
```python
>>> from transformers import AutoProcessor, AutoModelForImageTextToText
>>> import torch
>>> torch_device = "cuda"
>>> model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
>>> processor = AutoProcessor.from_pretrained(model_checkpoint)
>>> model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
>>> messages = [
... [
... {
... "role": "user",
... "content": [
... {"type": "image", "url": "https://llava-vl.github.io/static/images/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, dtype=torch.bfloat16)
>>> 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"]
```
### Batched multi-image input and quantization with BitsAndBytes
This implementation of the Mistral3 models supports batched text-images inputs with different number of images for each text.
This example also how to use `BitsAndBytes` to load the model in 4bit quantization.
```python
>>> from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig
>>> import torch
>>> torch_device = "cuda"
>>> 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
... )
>>> messages = [
... [
... {
... "role": "user",
... "content": [
... {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
... {"type": "text", "text": "Write a haiku for this image"},
... ],
... },
... ],
... [
... {
... "role": "user",
... "content": [
... {"type": "image", "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"},
... {"type": "image", "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.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, dtype=torch.bfloat16)
>>> 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."]
```