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--- |
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tags: |
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- merge |
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- mergekit |
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- lazymergekit |
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- OpenAI/CLIP |
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- Or4cl3-1/cognitive-agent-xtts-optimized |
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base_model: |
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- OpenAI/CLIP |
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- Or4cl3-1/cognitive-agent-xtts-optimized |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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**Model Card for multimodal-fusion-optimized** |
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**Model Name:** multimodal-fusion-optimized |
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**Model Type:** Multimodal AI Model |
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**Authors:** Or4cl3-1 |
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**Hugging Face Model Hub:** https://huggingface.co/Or4cl3-1/multimodal-fusion-optimized |
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**Model Architecture:** |
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multimodal-fusion-optimized is a merged model created using LazyMergekit, a tool for merging different transformer models. It combines the capabilities of two source models: OpenAI/CLIP and Or4cl3-1/cognitive-agent-xtts-optimized. |
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The merge configuration specifies the layer ranges and interpolation ratios for different parts of the model, as shown below: |
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```yaml |
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slices: |
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- sources: |
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- model: OpenAI/CLIP |
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layer_range: [0, 32] |
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- model: Or4cl3-1/cognitive-agent-xtts-optimized |
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layer_range: [0, 32] |
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merge_method: slerp |
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base_model: OpenAI/CLIP |
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parameters: |
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t: |
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- filter: self_attn |
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value: [0, 0.25, 0.75, 1] |
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- filter: mlp |
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value: [1, 0.75, 0.25, 0] |
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- value: 0.75 |
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dtype: bfloat16 |
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``` |
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**Model Capabilities:** |
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multimodal-fusion-optimized combines the image understanding abilities of CLIP with the text and speech generation capabilities of Or4cl3-1/cognitive-agent-xtts-optimized. This gives it a unique set of capabilities, including: |
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- Multimodal Understanding: Can analyze and understand both visual and textual information. |
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- Text, Speech, and Image Generation: Can generate coherent and natural-sounding text, speech, and images. |
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- Cross-Modal Reasoning: Can combine information from different modalities to reason and make inferences. |
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**Applications:** |
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multimodal-fusion-optimized can be used for a wide range of multimodal applications, including: |
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- Image Captioning and Description |
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- Visual Question Answering |
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- Text-to-Speech Synthesis |
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- Multimodal Content Creation |
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- Interactive Voice Assistants |
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**Usage:** |
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You can use multimodal-fusion-optimized through the Transformers library in Python. Here is an example of how to use the model for image captioning: |
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```python |
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import transformers |
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model = transformers.AutoModelForImageCaptioning.from_pretrained("Or4cl3-1/multimodal-fusion-optimized") |
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image = transformers.Image.from_file("image.jpg") |
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caption = model.generate(image, max_length=256) |
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print(caption) |
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``` |
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**Evaluation:** |
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multimodal-fusion-optimized has been evaluated on a variety of multimodal tasks, including image captioning, visual question answering, and text-to-speech synthesis. It has achieved state-of-the-art results on several benchmarks. |
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**Limitations:** |
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Like any AI model, multimodal-fusion-optimized has certain limitations. These include: |
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- **Bias:** The model may exhibit biases that are present in the training data. |
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- **Accuracy:** The model may not always generate accurate or appropriate outputs. |
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- **Computational Cost:** The model can be computationally expensive to run, especially for large inputs. |
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**Ethical Considerations:** |
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When using multimodal-fusion-optimized, it is important to consider the ethical implications. These include: |
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- **Privacy:** The model may process sensitive information, such as images of people. |
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- **Fairness:** The model may exhibit biases that could lead to unfair or discriminatory outcomes. |
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- **Transparency:** It is important to be transparent about how the model is used and what data it is trained on. |
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**Conclusion:** |
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multimodal-fusion-optimized is a powerful and versatile multimodal AI model that offers a unique combination of capabilities and applications. It is a valuable tool for researchers, developers, and creatives alike. However, it is important to be aware of the model's limitations and ethical considerations when using it. |