Summarization
Transformers
PyTorch
Safetensors
multilingual
miscovery
transformer
translation
question-answering
english
arabic
Instructions to use miscovery/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miscovery/model with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="miscovery/model")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("miscovery/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 422 Bytes
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"architectures": [
"CustomTransformerModel"
],
"bos_token_id": 2,
"d_ff": 2048,
"d_model": 512,
"dropout": 0.05,
"eos_token_id": 3,
"max_position_embeddings": 300,
"model_type": "miscovery",
"num_decoder_layers": 8,
"num_encoder_layers": 8,
"num_heads": 8,
"pad_token_id": 0,
"torch_dtype": "float32",
"transformers_version": "4.35.2",
"use_flash_attn": true,
"vocab_size": 50000
}
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