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---
library_name: transformers
language:
- ru
- en
license: apache-2.0
---
# Релиз вихря 0.5*
Долили сильно больше данных в sft, теперь стабильнее работает json и multiturn, слегка подточили параметры претрена модели, добавили RoPE на 32к контекста
Added a lot more data to sft, now json and multiturn work more stable on long context and hard prompts
- [Google Colab] - later
- [GGUF](https://huggingface.co/Vikhrmodels/it-5.3-fp16-32k-GGUF)
- [EXL2](https://huggingface.co/Vikhrmodels/it-5.3-fp16-32k-EXL2)
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/it-5.3-fp16-32k",
device_map="auto",
attn_implementation="sdpa",
torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("Vikhrmodels/it-5.3-fp16-32k")
from transformers import AutoTokenizer, pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
prompts = [
"В чем разница между фруктом и овощем?",
"Годы жизни колмагорова?"]
def test_inference(prompt):
prompt = pipe.tokenizer.apply_chat_template([{"role": "user", "content": prompt}], tokenize=False, add_generation_prompt=True)
print(prompt)
outputs = pipe(prompt, max_new_tokens=512, do_sample=True, num_beams=1, temperature=0.25, top_k=50, top_p=0.98, eos_token_id=79097)
return outputs[0]['generated_text'][len(prompt):].strip()
for prompt in prompts:
print(f" prompt:\n{prompt}")
print(f" response:\n{test_inference(prompt)}")
print("-"*50)
```
```
@article{nikolich2024vikhr,
title={Vikhr: The Family of Open-Source Instruction-Tuned Large Language Models for Russian},
author={Aleksandr Nikolich and Konstantin Korolev and Artem Shelmanov},
journal={arXiv preprint arXiv:2405.13929},
year={2024},
url={https://arxiv.org/pdf/2405.13929}
}
``` |