---
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- transformers.js
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
Code to generate this model:

```py
from transformers import PaliGemmaForConditionalGeneration, PaliGemmaConfig, SiglipVisionConfig, GemmaConfig

shared_dim = 16

# Initializing a Siglip-like vision config
vision_config = SiglipVisionConfig(
    hidden_size=16,
    intermediate_size=32,
    num_attention_heads=16,
    num_hidden_layers=2,
    num_image_tokens=256,
    patch_size=14,
    projection_dim=shared_dim,
    projector_hidden_act="gelu_fast",
    vision_use_head=False,
)

# Initializing a PaliGemma config
text_config = GemmaConfig(
    hidden_size=shared_dim,
    intermediate_size=16,
    num_attention_heads=4,
    num_hidden_layers=2,
    num_image_tokens=256,
    num_key_value_heads=1,
    vocab_size=257216,
)

# Initializing a PaliGemma paligemma-3b-224 style configuration
configuration = PaliGemmaConfig(
    vision_config,
    text_config,
    bos_token_id=2,
    eos_token_id=1,
    hidden_size=shared_dim,
    ignore_index=-100,
    image_token_index=257152,
    pad_token_id=0,
    projection_dim=shared_dim,
)

# Initializing a model from the paligemma-3b-224 style configuration
model = PaliGemmaForConditionalGeneration(configuration)

# Push to the Hub
model.push_to_hub('Xenova/tiny-random-PaliGemmaForConditionalGeneration')
```

Followed by:
```py
from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained("google/paligemma-3b-mix-224")
processor.push_to_hub('Xenova/tiny-random-PaliGemmaForConditionalGeneration')
```

## Model Details

### Model Description

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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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## How to Get Started with the Model

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## Training Details

### Training Data

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## Environmental Impact

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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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