Chirp-01

Name: Chirp-01
Rating: 63.5 (10 reviews)
Author: ozone-research

—

ozone-research

Other

OTHER

2B params

New

14 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

5GB+ RAM

Mobile

Laptop

Server

Quick Summary

Chirp-3b is a high-performing 3B parameter language model crafted by the Ozone Research team.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

2GB+ RAM

Code Examples

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloadpythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-research/Chirp-01"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What’s the future of AI?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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