Welcome to the world of AI-driven conversational agents! If you’re interested in creating your own chatbot using the Gemma-2 9B Instruct model, you’ve come to the right place. In this guide, we will walk you through the process of setting up and using the Saiga/Gemma2 9B, a Russian-language chatbot designed to assist users with various queries.
Understanding the Prompt Format
To effectively communicate with the Saiga/Gemma2 chatbot, you must adhere to a specific prompt format. Imagine you’re sending a message in a well-structured email; it needs an introduction, a clear query, and a closing. Here’s what that looks like:
<start_of_turn>systemТы — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.</end_of_turn>
<start_of_turn>userКак дела?</end_of_turn>
<start_of_turn>modelОтлично, а у тебя?</end_of_turn>
<start_of_turn>userШикарно. Как пройти в библиотеку?</end_of_turn>
<start_of_turn>model</code>This structure allows the model to recognize different roles in the conversation, providing more relevant responses.
Getting Started with the Code
In order to use the Saiga/Gemma2 chatbot, you'll need to run some Python code. Think of it as preparing a recipe: you need the ingredients (code libraries) and the instructions (code snippets).
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
MODEL_NAME = "IlyaGusev/saiga_gemma2_10b"
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
load_in_8bit=True,
torch_dtype=torch.bfloat16,
device_map="auto"
)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
inputs = ["Почему трава зеленая?", "Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч"]
for query in inputs:
prompt = tokenizer.apply_chat_template([{
"role": "user",
"content": query
}], tokenize=False, add_generation_prompt=True)
data = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
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).strip()
print(query)
print(output)
print()
print("==============================")
print()In this code:
- Think of
modelas your chef, preparing responses based on the ingredients (queries) you provide. - The
tokenizeracts like a script that converts your ingredients into a format the chef understands. - The loop goes through each query, allowing the chef to whip up a tasty response!
Troubleshooting Common Issues
While working with AI models can be exciting, you may run into a few bumps along the way. Here are some common issues and how to resolve them:
- Model not found: Ensure you are using the correct model name
IlyaGusev/saiga_gemma2_10b. If issues persist, double-check your internet connection. - Memory errors: This can happen if your system does not have enough memory. Try lowering the
load_in_8bitflag or optimizing your code. - Error messages during installation: Make sure to install the required libraries, such as
torchandtransformers.
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Conclusion
Using the Saiga/Gemma2 9B chatbot is like crafting your friendly virtual assistant, ready to answer questions and engage in conversation in Russian. By following this guide, you are well on your way to creating engaging interactions with AI.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.