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ibm-granite-vision-3.2-2b-fp16

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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-### Direct Use
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ---
+base_model:
+- ibm-granite/granite-vision-3.2-2b
 ---
+# MISHANM/deepseek-ai_janus-Pro-7B-fp16
+The MISHANM/ibm-granite-granite-vision-3.2-2b-fp16 model is a sophisticated vision-language model designed for image-to-text generation. It leverages advanced neural architectures to transform visual inputs into coherent textual descriptions.
+## Model Details
+1. Language: English
+2. Tasks: Imgae to Text Generation
+### Model Example output
+This is the model inference output:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66851b2c4461866b07738832/QeQENKNaU9VoaFhYvdXBs.png)
+ ## Getting Started
+ To begin using the model, ensure you have the necessary dependencies:
+```shell
+pip install transformers>=4.49
+```
+## Use the code below to get started with the model.
+Using Gradio
+```python
+import gradio as gr
+from transformers import AutoProcessor, AutoModelForVision2Seq
+import torch
+from PIL import Image
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_path = "MISHANM/ibm-granite-granite-vision-3.2-2b-fp16"
+processor = AutoProcessor.from_pretrained(model_path)
+model = AutoModelForVision2Seq.from_pretrained(model_path, ignore_mismatched_sizes=True).to(device)
+def process_image_and_prompt(image_path, prompt):
+    # Load the image
+    image = Image.open(image_path).convert("RGB")
+    # Prepare the conversation input
+    conversation = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "url": image},
+                {"type": "text", "text": prompt},
+            ],
+        },
+    ]
+    # Process the inputs
+    inputs = processor.apply_chat_template(
+        conversation,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt"
+    ).to(device)
+    # Generate the output
+    output = model.generate(**inputs, max_new_tokens=100)
+    return processor.decode(output[0], skip_special_tokens=True)
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=process_image_and_prompt,
+    inputs=[
+        gr.Image(type="filepath", label="Upload Image"),
+        gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt")
+    ],
+    outputs="text",
+    title="Granite Vision: Advanced Image-to-Text Generation Model",
+    description="Upload an image and enter a text prompt to get a response from the model."
+)
+# Launch the Gradio app
+iface.launch(share=True)
+```
+## Uses
+### Direct Use
+This model is ideal for converting images into descriptive text, making it valuable for creative projects, content creation, and artistic exploration.
+### Out-of-Scope Use
+The model is not intended for generating explicit or harmful content. It may also face challenges with highly abstract or nonsensical prompts.
+## Bias, Risks, and Limitations
+The model may reflect biases present in its training data, potentially resulting in stereotypical or biased outputs. Users should be aware of these limitations and review generated content for accuracy and appropriateness.
+### Recommendations
+Users are encouraged to critically evaluate the model's outputs, especially in sensitive contexts, to ensure they meet the desired standards of accuracy and appropriateness.
+## Citation Information
+```
+@misc{MISHANM/ibm-granite-granite-vision-3.2-2b-fp16,
+  author = {Mishan Maurya},
+  title = {Introducing Image to Text Generation model},
+  year = {2025},
+  publisher = {Hugging Face},
+  journal = {Hugging Face repository},
+}
+```