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README.md

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+## Model Card for Custom Minimal Transformer
+
+### Model Description
+This is a custom transformer model designed for educational purposes. It demonstrates the basic structure of a transformer model using PyTorch and integrates a pre-trained tokenizer from the Hugging Face library (`bert-base-uncased`).
+
+### Architecture
+The model, `MinimalTransformer`, is a simplified transformer architecture consisting of:
+- Multi-head attention mechanism (`nn.MultiheadAttention`).
+- Layer normalization (`nn.LayerNorm`).
+- A feed-forward network composed of linear layers and ReLU activation.
+
+It demonstrates basic transformer concepts while being more lightweight and easier to understand than full-scale models like BERT or GPT.
+
+### Training
+The model was trained on a small, manually created dataset consisting of simple sentences like "Hello world", "Transformers are great", and "PyTorch is fun". It's intended for basic demonstrations and not for achieving state-of-the-art results on complex tasks.
+
+### Tokenizer
+The tokenizer used is the `AutoTokenizer` from Hugging Face, specifically the "bert-base-uncased" variant. It handles tokenization, adding special tokens, and converting tokens to their respective IDs in the BERT vocabulary.
+
+### Usage
+The model can be used for basic NLP tasks and demonstrations. To use the model:
+- Load the saved model weights into the `MinimalTransformer` architecture.
+- Tokenize input sentences using the provided tokenizer.
+- Pass the tokenized input through the model for inference.
+
+### Limitations and Bias
+- The model's performance is limited due to its simplistic nature and the small training dataset.
+- As it uses a pre-trained BERT tokenizer, any biases present in the BERT model may be transferred to this model.
+
+### Acknowledgements
+This model was created for educational purposes and is based on the PyTorch and Hugging Face Transformers libraries.

config.json

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+{
+    "embed_size": 128,
+    "heads": 8,
+    "forward_expansion": 4,
+    "vocab_size": 30522
+}

hello-base-model.bin

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+size 15630675

hello-base-model.safetensors

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}