custom-57m-language-model

3

1 language

license:mit

by

Mostafa8Mehrabi

Language Model

OTHER

New

3 downloads

Early-stage

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Quick Summary

A custom 57.55M parameter causal language model with modern transformer architecture. - Parameters: 57,553,632 (57.55M) - Architecture: 12-layer Transformer -...

Code Examples

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Generation Parameterspythontransformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/custom-57m-language-model")
model = AutoModelForCausalLM.from_pretrained("your-username/custom-57m-language-model")

input_text = "The future of artificial intelligence"
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(
    inputs, 
    max_length=100, 
    temperature=0.8,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.1
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

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