In the ever-evolving landscape of artificial intelligence, the Hare-1.1B-base, developed by the LiteAI Team from China Telecom Guizhou Branch, stands as a remarkable text generation model. This guide will walk you through how to deploy and utilize this model effectively.

Understanding the Basics of Hare-1.1B-base

The Hare-1.1B-base is compared to a compact yet efficient vehicle. Although it has only 1.1 billion parameters (like a car with a modest engine), it performs excellently on various benchmarks, like climbing steep hills with ease thanks to its construction using high-quality open-source and strategy-generated data. The model is designed with a Mistral architecture and optimized with specific hyperparameters, enabling it to fit seamlessly into various AI projects, just as a small car can navigate urban traffic efficiently.

Steps to Implement Hare-1.1B-base in Your Projects

1. Installation: Start by installing the required Python packages.
• To use Hare with VLLM:

pip install vllm

2. Load the Model: Utilize the following Python code snippet to load the Hare model.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_path = "LiteAI-Team/Hare-1.1B-base"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
model.to(device)
    

3. Generate Text: After loading the model, you can generate text using a simple prompt.

prompt = "Write a poem based on the landscape of Guizhou:"
tokens = tokenizer(prompt, add_special_tokens=True, return_tensors="pt").to(device)
output = model.generate(**tokens, max_new_tokens=128)
output_tokens = output[0].cpu().numpy()[tokens.input_ids.size()[1]:]
output_string = tokenizer.decode(output_tokens)
print(output_string)
    

Deploying on Edge Devices

The compact size of Hare-1.1B-base (which occupies only 0.6 GB after Int4 quantization) allows for easy deployment on mobile devices. The LiteAI Team conducted tests using the MLC-LLM framework, making it accessible on various platforms including Android and iOS.

Troubleshooting

• Issue: Model not loading successfully.
• Solution: Ensure you have the correct version of PyTorch and Transformers installed. You may also check your internet connection if the model fails to download.
• Issue: Performance lags during text generation.
• Solution: Run the model on a CUDA-capable device or optimize your input tokens.
• Issue: Unexpected output or errors in generated text.
• Solution: Adjust the prompt for clarity or simplify the query. Additionally, reviewing your hyperparameters may help enhance performance.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

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.