Files
Vikhr-7B-instruct_0.4/README.md
ModelHub XC ee44ef427c 初始化项目,由ModelHub XC社区提供模型
Model: Vikhrmodels/Vikhr-7B-instruct_0.4
Source: Original Platform
2026-06-07 06:52:14 +08:00

57 lines
2.1 KiB
Markdown
Raw Permalink Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

---
library_name: transformers
language:
- ru
- en
---
# Релиз вихря 0.3-0.4
Долили сильно больше данных в sft, теперь стабильнее работает json и multiturn, слегка подточили параметры претрена модели
Added a lot more data to sft, now json and multiturn work more stable on long context and hard prompts
- [Google Colab](https://colab.research.google.com/drive/15O9LwZhVUa1LWhZa2UKr_B-KOKenJBvv#scrollTo=5EeNFU2-9ERi)
- [GGUF](https://huggingface.co/Vikhrmodels/Vikhr-7B-instruct_0.4-GGUF)
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Vikhr-7B-instruct_0.4",
device_map="auto",
attn_implementation="flash_attention_2",
torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("Vikhrmodels/Vikhr-7B-instruct_0.4")
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}
}
```