QVikhr-3-8B-Instruction

3.0K

8

8.0B

2 languages

license:apache-2.0

by

Vikhrmodels

Language Model

OTHER

8B params

New

3K downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

18GB+ RAM

Mobile

Laptop

Server

Quick Summary

Инструктивная модель на основе Qwen/Qwen3-8B, обученная на русскоязычном датасете GrandMaster2.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

8GB+ RAM

Code Examples

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Пример кода для запуска:pythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "Vikhrmodels/QVikhr-3-8B-Instruction"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare the input text
input_text = "Напиши краткое описание книги Гарри Поттер."

messages = [
    {"role": "user", "content": input_text},
]

# Tokenize and generate text
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids,
    max_length=4096,
    temperature=0.3,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    top_k=50,
    top_p=0.95,
)

# Decode and print result
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

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