105 lines
3.2 KiB
Markdown
105 lines
3.2 KiB
Markdown
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---
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language:
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- ru
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- en
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datasets:
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- zjkarina/Vikhr_instruct
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- dichspace/darulm
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---
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GGUF версия: https://huggingface.co/pirbis/Vikhr-7B-instruct_0.2-GGUF
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```python
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from peft import PeftModel, PeftConfig
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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import torch
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import os
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os.environ['HF_HOME']='.'
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MODEL_NAME = "Vikhrmodels/Vikhr-7B-instruct_0.2"
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DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>\n"
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DEFAULT_SYSTEM_PROMPT = "Ты — Вихрь, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."
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class Conversation:
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def __init__(
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self,
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message_template=DEFAULT_MESSAGE_TEMPLATE,
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system_prompt=DEFAULT_SYSTEM_PROMPT,
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):
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self.message_template = message_template
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self.messages = [{
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"role": "system",
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"content": system_prompt
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}]
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def add_user_message(self, message):
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self.messages.append({
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"role": "user",
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"content": message
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})
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def get_prompt(self, tokenizer):
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final_text = ""
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for message in self.messages:
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message_text = self.message_template.format(**message)
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final_text += message_text
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final_text += 'bot'
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return final_text.strip()
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def generate(model, tokenizer, prompt, generation_config):
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data = tokenizer(prompt, return_tensors="pt")
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data = {k: v.to(model.device) for k, v in data.items()}
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output_ids = model.generate(
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**data,
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generation_config=generation_config
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)[0]
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output_ids = output_ids[len(data["input_ids"][0]):]
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output = tokenizer.decode(output_ids, skip_special_tokens=True)
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return output.strip()
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#config = PeftConfig.from_pretrained(MODEL_NAME)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_NAME,
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load_in_8bit=True,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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#model = PeftModel.from_pretrained( model, MODEL_NAME, torch_dtype=torch.float16)
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model.eval()
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False)
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generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
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generation_config.max_length=256
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generation_config.top_p=0.9
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generation_config.top_k=30
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generation_config.do_sample = True
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print(generation_config)
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inputs = ["Как тебя зовут?", "Кто такой Колмогоров?"]
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for inp in inputs:
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conversation = Conversation()
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conversation.add_user_message(inp)
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prompt = conversation.get_prompt(tokenizer)
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output = generate(model, tokenizer, prompt, generation_config)
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print(inp)
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print(output)
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print('\n')
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```
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[wandb](https://wandb.ai/karina_romanova/vikhr/runs/up2hw5eh?workspace=user-karina_romanova)
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## Cite
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```
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@inproceedings{nikolich2024vikhr,
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title={Vikhr: Constructing a State-of-the-art Bilingual Open-Source Instruction-Following Large Language Model for {Russian}},
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author={Aleksandr Nikolich and Konstantin Korolev and Sergei Bratchikov and Igor Kiselev and Artem Shelmanov },
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booktitle = {Proceedings of the 4rd Workshop on Multilingual Representation Learning (MRL) @ EMNLP-2024}
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year={2024},
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publisher = {Association for Computational Linguistics},
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url={https://arxiv.org/pdf/2405.13929}
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}
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```
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