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---
license: apache-2.0
library_name: vllm
pipeline_tag: image-text-to-text
tags:
- int4
- vllm
- llmcompressor
base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
---
# Mistral-Small-3.1-24B-Instruct-2503-GPTQ-4b-128g
## Model Overview
This model was obtained by quantizing the weights of [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
Only the weights of the linear operators within `language_model` transformers blocks are quantized. Vision model and multimodal projection are kept in original precision. Weights are quantized using a symmetric per-group scheme, with group size 128. The GPTQ algorithm is applied for quantization.
Model checkpoint is saved in [compressed_tensors](https://github.com/neuralmagic/compressed-tensors) format.
## Usage
* To use the model in `transformers` update the package to stable release of Mistral-3
`pip install git+https://github.com/huggingface/[email protected]`
* To use the model in `vLLM` update the package to version `vllm>=0.8.0`.
And example of inference via transformers is provided below:
```python
# pip install accelerate
from transformers import AutoProcessor, AutoModelForImageTextToText
from PIL import Image
import requests
import torch
model_id = "ISTA-DASLab/Mistral-Small-3.1-24B-Instruct-2503-GPTQ-4b-128g"
model = AutoModelForImageTextToText.from_pretrained(
model_id, device_map="auto"
).eval()
processor = AutoProcessor.from_pretrained(model_id)
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are a helpful assistant."}]
},
{
"role": "user",
"content": [
{"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
{"type": "text", "text": "Describe this image in detail."}
]
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.bfloat16)
input_len = inputs["input_ids"].shape[-1]
with torch.inference_mode():
generation = model.generate(**inputs, max_new_tokens=100, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
``` |