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hf_model/README.md

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+# Mini GPT1 Clone
+
+This is a decoder-only transformer model (GPT1-style) trained from scratch using PyTorch.
+
+## Model Details
+
+- **Architecture**: Decoder-only Transformer
+- **Layers**: 6
+- **Embedding Size**: 512
+- **Heads**: 8
+- **Feedforward Dim**: 2048
+- **Sequence Length**: 256
+- **Vocab Size**: 35,000
+
+## Tokenizer
+
+Trained using `ByteLevelBPETokenizer` from the `tokenizers` library.
+
+## Inference Example
+
+```python
+from transformers import PreTrainedTokenizerFast, AutoModelForCausalLM
+import torch
+
+tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer/tokenizer.json")
+model = AutoModelForCausalLM.from_pretrained("dilip025/mini-gpt1")
+
+prompt = "Once upon a time,"
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+outputs = model.generate(input_ids, max_length=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+License
+MIT