Spec-T1-Base-7B

Name: Spec-T1-Base-7B
Author: SVECTOR-CORPORATION

7.0B

1 language

—

SVECTOR-CORPORATION

Language Model

OTHER

7B params

New

0 downloads

Early-stage

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Edge AI:

Mobile

Laptop

Server

16GB+ RAM

Mobile

Laptop

Server

Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

7GB+ RAM

Code Examples

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-T1-Base-7B")

prompt = """
Explain why the sum of the first n odd numbers equals n^2.
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=512,
    temperature=0.6,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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