Instructions to use tiny-random/mistral-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/mistral-3 with Transformers:

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

pipe = pipeline("image-text-to-text", model="tiny-random/mistral-3")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("tiny-random/mistral-3")
model = AutoModelForImageTextToText.from_pretrained("tiny-random/mistral-3")
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

vLLM

How to use tiny-random/mistral-3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/mistral-3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/mistral-3",
		"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/tiny-random/mistral-3

SGLang

How to use tiny-random/mistral-3 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 "tiny-random/mistral-3" \
    --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": "tiny-random/mistral-3",
		"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 "tiny-random/mistral-3" \
        --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": "tiny-random/mistral-3",
		"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 tiny-random/mistral-3 with Docker Model Runner:
```
docker model run hf.co/tiny-random/mistral-3
```

mistral-3

File size: 1,510 Bytes

931aa2e

{
  "architectures": [
    "Mistral3ForConditionalGeneration"
  ],
  "dtype": "bfloat16",
  "image_token_index": 10,
  "model_type": "mistral3",
  "multimodal_projector_bias": false,
  "projector_hidden_act": "gelu",
  "spatial_merge_size": 2,
  "text_config": {
    "attention_dropout": 0.0,
    "head_dim": 32,
    "hidden_act": "silu",
    "hidden_size": 8,
    "initializer_range": 0.02,
    "intermediate_size": 64,
    "max_position_embeddings": 262144,
    "model_type": "ministral3",
    "num_attention_heads": 8,
    "num_hidden_layers": 2,
    "num_key_value_heads": 4,
    "rms_norm_eps": 1e-05,
    "rope_parameters": {
      "beta_fast": 32.0,
      "beta_slow": 1.0,
      "factor": 16.0,
      "llama_4_scaling_beta": 0.1,
      "mscale": 1.0,
      "mscale_all_dim": 1.0,
      "original_max_position_embeddings": 16384,
      "rope_theta": 1000000000.0,
      "rope_type": "yarn",
      "type": "yarn"
    },
    "sliding_window": null,
    "use_cache": true,
    "vocab_size": 131072
  },
  "transformers_version": "5.0.0.dev0",
  "vision_config": {
    "attention_dropout": 0.0,
    "head_dim": 32,
    "hidden_act": "silu",
    "hidden_size": 128,
    "image_size": 1540,
    "initializer_range": 0.02,
    "intermediate_size": 128,
    "model_type": "pixtral",
    "num_attention_heads": 4,
    "num_channels": 3,
    "num_hidden_layers": 2,
    "patch_size": 14,
    "rope_parameters": {
      "rope_theta": 10000.0,
      "rope_type": "default"
    }
  },
  "vision_feature_layer": -1
}