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README.md
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---
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datasets:
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- IlyaGusev/ru_turbo_alpaca
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- IlyaGusev/ru_turbo_saiga
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- IlyaGusev/ru_sharegpt_cleaned
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- IlyaGusev/oasst1_ru_main_branch
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- IlyaGusev/ru_turbo_alpaca_evol_instruct
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- lksy/ru_instruct_gpt4
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language:
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- ru
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pipeline_tag: conversational
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license: cc-by-4.0
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---
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# Saiga2 7B, Russian LLaMA2-based chatbot
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Based on [Mistral OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca).
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This is an adapter-only version.
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Llama.cpp version: TBA
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Colab: TBA
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Training code: [link](https://github.com/IlyaGusev/rulm/tree/master/self_instruct).
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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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MODEL_NAME = "IlyaGusev/saiga_mistral_7b"
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DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>"
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DEFAULT_RESPONSE_TEMPLATE = "<s>bot\n"
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DEFAULT_SYSTEM_PROMPT = "Ты — Сайга, 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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start_token_id=1,
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):
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self.message_template = message_template
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self.start_token_id = start_token_id
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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 get_start_token_id(self):
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return self.start_token_id
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def get_bot_token_id(self):
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return self.bot_token_id
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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 add_bot_message(self, message):
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self.messages.append({
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"role": "bot",
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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 += tokenizer.decode([self.start_token_id, self.bot_token_id])
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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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config.base_model_name_or_path,
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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(
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model,
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MODEL_NAME,
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torch_dtype=torch.float16
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)
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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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print(generation_config)
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inputs = ["Почему трава зеленая?", "Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч"]
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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()
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print("==============================")
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print()
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```
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Examples:
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```
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User: Почему трава зеленая?
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Saiga: ```
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```
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User: Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч
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Saiga:
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```
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- dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a
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- wandb [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/ip1qmm9p)
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- 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4
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- Datasets merging script: [create_short_chat_set.py](https://github.com/IlyaGusev/rulm/blob/d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a/self_instruct/src/data_processing/create_short_chat_set.py)
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- saiga_mistral_7b vs saiga2_13b: 243-31-141
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