File size: 3,325 Bytes
2254d94 9b27654 4cc5d0d 2254d94 16ff3b8 9b27654 7b0388d 8ce1638 9b27654 7b0388d be72e85 9b27654 7b0388d 9b27654 be72e85 9b27654 7b0388d 9b27654 7b0388d 9b27654 094fb0c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
---
language:
- ru
- en
datasets:
- zjkarina/Vikhr_instruct
- dichspace/darulm
---
GGUF версия: https://huggingface.co/pirbis/Vikhr-7B-instruct_0.2-GGUF
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch
import os
os.environ['HF_HOME']='.'
MODEL_NAME = "Vikhrmodels/Vikhr-7B-instruct_0.2"
DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>\n"
DEFAULT_SYSTEM_PROMPT = "Ты — Вихрь, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."
class Conversation:
def __init__(
self,
message_template=DEFAULT_MESSAGE_TEMPLATE,
system_prompt=DEFAULT_SYSTEM_PROMPT,
):
self.message_template = message_template
self.messages = [{
"role": "system",
"content": system_prompt
}]
def add_user_message(self, message):
self.messages.append({
"role": "user",
"content": message
})
def get_prompt(self, tokenizer):
final_text = ""
for message in self.messages:
message_text = self.message_template.format(**message)
final_text += message_text
final_text += 'bot'
return final_text.strip()
def generate(model, tokenizer, prompt, generation_config):
data = tokenizer(prompt, return_tensors="pt")
data = {k: v.to(model.device) for k, v in data.items()}
output_ids = model.generate(
**data,
generation_config=generation_config
)[0]
output_ids = output_ids[len(data["input_ids"][0]):]
output = tokenizer.decode(output_ids, skip_special_tokens=True)
return output.strip()
#config = PeftConfig.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
load_in_8bit=True,
torch_dtype=torch.float16,
device_map="auto"
)
#model = PeftModel.from_pretrained( model, MODEL_NAME, torch_dtype=torch.float16)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False)
generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
generation_config.max_length=256
generation_config.top_p=0.9
generation_config.top_k=30
generation_config.do_sample = True
print(generation_config)
inputs = ["Как тебя зовут?", "Кто такой Колмогоров?"]
for inp in inputs:
conversation = Conversation()
conversation.add_user_message(inp)
prompt = conversation.get_prompt(tokenizer)
output = generate(model, tokenizer, prompt, generation_config)
print(inp)
print(output)
print('\n')
```
[wandb](https://wandb.ai/karina_romanova/vikhr/runs/up2hw5eh?workspace=user-karina_romanova)
## Cite
```
@inproceedings{nikolich2024vikhr,
title={Vikhr: Constructing a State-of-the-art Bilingual Open-Source Instruction-Following Large Language Model for {Russian}},
author={Aleksandr Nikolich and Konstantin Korolev and Sergei Bratchikov and Igor Kiselev and Artem Shelmanov },
booktitle = {Proceedings of the 4rd Workshop on Multilingual Representation Learning (MRL) @ EMNLP-2024}
year={2024},
publisher = {Association for Computational Linguistics},
url={https://arxiv.org/pdf/2405.13929}
}
``` |