Add transformers snippet

README.md

@@ -428,4 +428,32 @@ print(outputs[0].outputs[0].text)
 
 Transformers-compatible model weights are also uploaded (thanks a lot @cyrilvallez). 
 However the transformers implementation was **not throughly tested**, but only on "vibe-checks".
-Hence, we can only ensure 100% correct behavior when using the original weight format with vllm (see above).
+Hence, we can only ensure 100% correct behavior when using the original weight format with vllm (see above).
+
+You can use Mistral-Small-3.1-24B-Instruct-2503 with transformers as follows.
+
+```python
+from transformers import AutoProcessor, AutoModelForImageTextToText
+import 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)
+```