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| File Name | Purpose |
|---|---|
model.safetensors | Model weights (replaces pytorch_model.bin in newer versions). |
tokenizer_config.json | Configuration for the tokenizer (e.g., padding, truncation settings). |
vocab.json | Vocabulary file for the tokenizer. |
merges.txt | BPE merge rules (used by GPT-2's tokenizer). |
special_tokens_map.json | Defines special tokens. |
config.json | Model architecture configuration (layers, heads, etc.). |
generation_config.json | Default text-generation settings (temperature, top-k, etc.). |
training_args.bin | Training arguments (optional, not needed for inference). |
.DS_Store | (Ignore) macOS metadata file. |
1from transformers import GPT2LMHeadModel, GPT2Tokenizer
2
3# Load the fine-tuned model and tokenizer
4model = GPT2LMHeadModel.from_pretrained("path_to_model_directory")
5tokenizer = GPT2Tokenizer.from_pretrained("path_to_model_directory")
6
7# Example prompt
8prompt = "If you can keep your head when all about you"
9inputs = tokenizer(prompt, return_tensors="pt")
10
11# Generate text
12outputs = model.generate(
13 inputs["input_ids"],
14 max_length=100,
15 num_return_sequences=1,
16 do_sample=True,
17 top_k=50,
18 top_p=0.95,
19 temperature=0.9
20)
21
22# Print generated text
23print(tokenizer.decode(outputs[0], skip_special_tokens=True))