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README.md
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@@ -70,10 +70,9 @@ GPT-124M is a lightweight generative language model fine-tuned on the `fineweb-e
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- **Validation Dataset:** 100 million tokens of HuggingFaceFW/fineweb-edu
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## Usage
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### Direct Use
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You can use this model for text generation using the `transformers` library.
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```python
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# Import necessary modules from transformers
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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# Load tokenizer and model
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model_name = "samkeet/GPT_124M"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
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# Create text generation pipeline
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pipe = pipeline("text-generation", model=
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# Generate text
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result = pipe("Earth revolves around the", do_sample=True, max_length=40, temperature=0.9, top_p=0.5, top_k=50)
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print(result)
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```
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### Fine-tuning & Downstream Use
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```
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## Contact
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For inquiries, contact [Samkeet Sangai](https://www.linkedin.com/in/samkeet-sangai/).
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- **Validation Dataset:** 100 million tokens of HuggingFaceFW/fineweb-edu
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## Usage
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You can use this model for text generation using the `transformers` library.
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### Method 1: Using Pipeline
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```python
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# Import necessary modules from transformers
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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# Load tokenizer and model
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model_name = "samkeet/GPT_124M"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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# Create text generation pipeline
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pipe = pipeline("text-generation", model=model_name, tokenizer=tokenizer, trust_remote_code=True, device="cpu")
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# Generate text
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result = pipe("Earth revolves around the", do_sample=True, max_length=40, temperature=0.9, top_p=0.5, top_k=50)
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print("Pipeline Output:", result)
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```
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### Method 1: Direct Generation
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```python
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# Import necessary libraries
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import torch
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# Function for direct tokenization and text generation
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def generate_text(input_text, device):
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tokens = tokenizer.encode(input_text, return_tensors='pt').to(device)
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model.to(device)
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# Generate output
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output = model.generate(
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tokens,
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do_sample=True,
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max_length=40,
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temperature=0.9,
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top_p=0.5,
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top_k=50,
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)
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# Decode generated text
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generated_sentence = tokenizer.decode(output)
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return generated_sentence
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# Generate text
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input_text = "Earth revolves around the"
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print("Direct Model Output:", generate_text(input_text))
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```
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### Fine-tuning & Downstream Use
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```
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## Contact
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For inquiries, contact [Samkeet Sangai](https://www.linkedin.com/in/samkeet-sangai/).
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