In the world of artificial intelligence, large language models (LLMs) are taking center stage. One such model is Yi, a next-generation bilingual language model that is generating waves for its impressive capabilities in language understanding, commonsense reasoning, and reading comprehension. If you’re keen to dive into the world of Yi, this guide will walk you through how to get started and troubleshoot potential hiccups along the way.

What is Yi?

Yi is an open-source large language model trained by 01.AI, designed to understand multiple languages. Its innovative architecture, inspired by the Transformer model, allows it to shine in various benchmarks, such as the AlpacaEval Leaderboard. With Yi-34B-Chat recently scoring second place behind GPT-4 Turbo, there’s much to explore!

How to Use Yi

Getting started with Yi models is simpler than you might think! Here’s your step-by-step guide on how to make Yi your own:

Quick Start

• Choose Your Path: Whether deploying locally or using APIs, your journey begins here.
• Local Deployment: For those equipped with sufficient resources (think NVIDIA A800), options include:
  • Pip Installation
  • Docker Deployment
  • Conda Lock
• Using APIs: Explore Yi’s capabilities via its official APIs or through platforms such as Replicate .
• Web Demo: For a quicker fix, engage with Yi using web applications directly.

Installation via Pip

Follow these steps to set up Yi-34B-Chat locally:

1. Ensure Python 3.10 or later is installed.
2. Clone the Yi repository:

   git clone https://github.com/01-ai/Yi.git

3. Navigate to the repository:

   cd Yi

4. Install required packages:

   pip install -r requirements.txt

5. Download the Yi model weights from sources like Hugging Face, ModelScope, or WiseModel.
6. Run your inference script as shown in the next section.

Performing Inference with Yi

Think of performing inference with a model like conducting an orchestra. Each musician (input) plays their instrument (code) at the right moment to create a harmonious output (response). Here’s how to get your Yi model to play its best tune:

Sample Code

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = 'your-model-path'
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_path).eval()

messages = [{'role': 'user', 'content': 'hi'}]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids)
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

print(response)  # Expect: "Hello! How can I assist you today?"

Troubleshooting Common Issues

While using Yi, you might encounter some bumps along the road. Here are some common troubleshooting tips:

• Model Not Loading: Ensure you have the correct path to the model weights and that your environment meets the hardware requirements.
• Performance Dips: If the model responds slowly, check your hardware specifications. For high-density models, better GPUs are essential.
• Inconsistent Responses: Adjust generation parameters like temperature or top_p to achieve more coherent messages.

For further insights and collaboration on AI projects, remember to stay connected with fxis.ai.

Wrap-up

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.

Now that you’re equipped with the knowledge to harness the power of Yi, dive in and explore all that this incredible model has to offer!