create model structure

README.md

@@ -1 +1,2 @@
-#Testing local upload
+#Testing local upload
+

config.json

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+{
+  "activation": "relu",
+  "architectures": [
+    "DistilBertForMaskedLM"
+  ],
+  "attention_dropout": 0.4,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "transformers_version": "4.47.1",
+  "vocab_size": 30522
+}

main.py

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+from transformers import DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizerFast, ViTImageProcessorFast, Wav2Vec2FeatureExtractor, Wav2Vec2Processor
+
+config = DistilBertConfig.from_pretrained("./config.json")
+model = DistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased",config=config)
+tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert/distilbert-base-uncased")
+vit_extractor = ViTImageProcessorFast.from_pretrained("google/vit-base-patch16-224")
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
+processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)

pyproject.toml

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+[tool.poetry]
+name = "ghanabaai-model"
+version = "0.1.0"
+description = ""
+authors = ["Faith S.Appoh <[email protected]>"]
+readme = "README.md"
+package-mode = false
+
+[tool.poetry.dependencies]
+python = "^3.12"
+transformers = "^4.47.1"
+torch = "^2.5.1"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"