Instructions to use Kiffaz11/ministral3-3b-reasoning-torchao-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kiffaz11/ministral3-3b-reasoning-torchao-int4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Kiffaz11/ministral3-3b-reasoning-torchao-int4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Kiffaz11/ministral3-3b-reasoning-torchao-int4")
model = AutoModelForMultimodalLM.from_pretrained("Kiffaz11/ministral3-3b-reasoning-torchao-int4", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kiffaz11/ministral3-3b-reasoning-torchao-int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kiffaz11/ministral3-3b-reasoning-torchao-int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kiffaz11/ministral3-3b-reasoning-torchao-int4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Kiffaz11/ministral3-3b-reasoning-torchao-int4

SGLang

How to use Kiffaz11/ministral3-3b-reasoning-torchao-int4 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 "Kiffaz11/ministral3-3b-reasoning-torchao-int4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kiffaz11/ministral3-3b-reasoning-torchao-int4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Kiffaz11/ministral3-3b-reasoning-torchao-int4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kiffaz11/ministral3-3b-reasoning-torchao-int4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Kiffaz11/ministral3-3b-reasoning-torchao-int4 with Docker Model Runner:
```
docker model run hf.co/Kiffaz11/ministral3-3b-reasoning-torchao-int4
```

Configuration Parsing Warning:In config.json: "quantization_config.modules_to_not_convert" must be an array

Ministral 3 3B Reasoning TorchAO INT4

This repository contains a TorchAO INT4 weight-only quantized checkpoint of:

mistralai/Ministral-3-3B-Reasoning-2512

Quantization summary

Quantization backend: TorchAO
Quantization type: INT4 weight-only
Packing/layout used during working export: Int4TilePackedTo4dTensor
Targeted modules: language model attention and MLP Linear layers
Kept dense: embeddings, lm_head, vision tower, multimodal projector
Final checkpoint size: ~2.656 GiB
Original BF16 checkpoint size measured locally: ~14.339 GiB
Checkpoint size reduction: ~81.48%

Smoke test result

Test problem:

Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?

Expected answer: 72

Results:

Model	Parsed answer	Correct
Original BF16	72	yes
TorchAO INT4	72	yes

Local benchmark summary

Metric	Original BF16	TorchAO INT4
Checkpoint size GiB	14.3391	2.6557
Load time seconds	6.9686	9.8574
Generation latency seconds	1.0092	1.0404
Generated tok/s	64.4074	53.8238
Total tok/s including prompt	165.4775	151.8600
Peak VRAM allocated GiB	15.9948	11.4769
Peak VRAM reserved GiB	16.0820	14.9023
CPU RSS after load GiB	8.0434	8.0436

Notes

This checkpoint is intended as a TorchAO quantized research artifact. The first validation target was successful reload, reduced checkpoint size, lower VRAM allocation, and correctness on a smoke arithmetic task.

The quantized model may not be faster than BF16 on every GPU/backend. For this checkpoint, the main gain is storage and allocated VRAM reduction.

Downloads last month: 5

Safetensors

Model size

0.9B params

Tensor type

I32

BF16

Model tree for Kiffaz11/ministral3-3b-reasoning-torchao-int4

Base model

mistralai/Ministral-3-3B-Base-2512

Finetuned

mistralai/Ministral-3-3B-Reasoning-2512

Quantized

(30)

this model