Instructions to use Prarabdha/pixtral-12b-240910-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Prarabdha/pixtral-12b-240910-hf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Prarabdha/pixtral-12b-240910-hf")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Prarabdha/pixtral-12b-240910-hf")
model = AutoModelForImageTextToText.from_pretrained("Prarabdha/pixtral-12b-240910-hf")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Prarabdha/pixtral-12b-240910-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Prarabdha/pixtral-12b-240910-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Prarabdha/pixtral-12b-240910-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Prarabdha/pixtral-12b-240910-hf

SGLang

How to use Prarabdha/pixtral-12b-240910-hf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Prarabdha/pixtral-12b-240910-hf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Prarabdha/pixtral-12b-240910-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Prarabdha/pixtral-12b-240910-hf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Prarabdha/pixtral-12b-240910-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Prarabdha/pixtral-12b-240910-hf with Docker Model Runner:
```
docker model run hf.co/Prarabdha/pixtral-12b-240910-hf
```

Pixtral-12B-2409 - HuggingFace Transformers Compatible Weights

Model Overview

This repository contains the HuggingFace Transformers compatible weights for the Pixtral-12B-2409 multimodal model. The weights have been converted to ensure seamless integration with the Hugging Face Transformers library, allowing easy loading and usage in your projects.

Model Details

Original Model: Pixtral-12B-2409 by Mistral AI
Model Type: Multimodal Language Model
Parameters: 12B parameters + 400M parameter vision encoder
Sequence Length: 128k tokens
License: Apache 2.0

Key Features

Natively multimodal, trained with interleaved image and text data
Supports variable image sizes
Leading performance in its weight class on multimodal tasks
Maintains state-of-the-art performance on text-only benchmarks

Conversion Details

This repository provides the original Pixtral model weights converted to be fully compatible with the HuggingFace Transformers library. The conversion process ensures:

Seamless loading using from_pretrained()
Full compatibility with HuggingFace Transformers pipeline
No modifications to the original model weights or architecture

Installation

You can install the model using the Transformers library:

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

processor = AutoProcessor.from_pretrained("Prarabdha/pixtral-12b-240910-hf")
model = AutoModelForImageTextToText.from_pretrained("Prarabdha/pixtral-12b-240910-hf", torch_dtype=torch.float16, device_map="auto")

Example Usage

from PIL import Image
import requests

# Load an image
url = "https://example.com/sample-image.jpg"
image = Image.open(requests.get(url, stream=True).raw)

# Prepare conversation
conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "What is shown in this image?"},
        ],
    }
]

# Process and generate
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
inputs = processor(images=[image], text=prompt, return_tensors="pt")
generate_ids = model.generate(**inputs, max_new_tokens=30)
response = processor.batch_decode(generate_ids, skip_special_tokens=True)

Performance Benchmarks

Multimodal Benchmarks

Benchmark	Pixtral 12B	Qwen2 7B VL	LLaVA-OV 7B	Phi-3 Vision
MMMU (CoT)	52.5	47.6	45.1	40.3
Mathvista (CoT)	58.0	54.4	36.1	36.4
ChartQA (CoT)	81.8	38.6	67.1	72.0

(Full benchmark details available in the original model card)

Acknowledgements

A huge thank you to the Mistral team for creating and releasing the original Pixtral model.

Citation

If you use this model, please cite the original Mistral AI research.

License

This model is distributed under the Apache 2.0 License.

Original Model Card

For more comprehensive details, please refer to the original Mistral model card.

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Safetensors

Model size

13B params

Tensor type

BF16

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